@article{Miranda2024,
title = {Revolutionising the Quality of Life: The Role of Real-Time Sensing in Smart Cities},
author = {R. Miranda and C. Alves and R. Sousa and A. Chaves and L. Montenegro and H. Peixoto and D. Durães and R. Machado and A. Abelha and P. Novais and J. Machado},
doi = {10.3390/electronics13030550},
issn = {20799292},
year  = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Electronics (Switzerland)},
volume = {13},
number = {3},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {To further evolve urban quality of life, this paper explores the potential of crowdsensing and crowdsourcing in the context of smart cities. To aid urban planners and residents in understanding the nuances of day-to-day urban dynamics, we actively pursue the improvement of data visualisation tools that can adapt to changing conditions. An architecture was created and implemented that ensures secure and easy connectivity between various sources, such as a network of Internet of Things (IoT) devices, to merge with crowdsensing data and use them efficiently. In addition, we expanded the scope of our study to include the development of mobile and online applications, emphasizing the integration of autonomous and geo-surveillance. The main findings highlight the importance of sensor data in urban knowledge. Their incorporation via Tepresentational State Transfer (REST) Application Programming Interface (APIs) improves data access and informed decision-making, and dynamic data visualisation provides better insights. The geofencing of the application encourages community participation in urban planning and resource allocation, supporting sustainable urban innovation. © 2024 by the authors.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Silva2024155,
title = {Predictive quality model for customer defects},
author = {A. C. Silva and J. Machado and P. Sampaio},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195506444&doi=10.1108%2fTQM-09-2023-0302&partnerID=40&md5=568e7612936588c8b44daaa72b93058c},
doi = {10.1108/TQM-09-2023-0302},
issn = {17542731},
year  = {2024},
date = {2024-01-01},
journal = {TQM Journal},
volume = {36},
number = {9},
pages = {155-174},
publisher = {Emerald Publishing},
abstract = {Purpose: In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations. Design/methodology/approach: To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings. Findings: The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0. Originality/value: This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization. © 2024, Anabela Costa Silva, José Machado and Paulo Sampaio.},
note = {cited By 0},
keywords = {Artificial intelligence techniques; Customer complaints; Data accessibility; Decisions makings; Digital transformation; Digitisation; Machine learning and customer complaint; Machine-learning; Predictive models; Quality 4.0, Data mining; Forecasting; Industry 4.0; Learning systems; Machine learning; Metadata; Quality control; Quality management; Sales, Decision making},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Sousa2024467,
title = {The Interplay of Inflation, Healthcare Spending, and Suicide Rates: An Empirical Analysis},
author = {R. Sousa and C. Ribeiro and C. Cardoso and B. Freixo and H. Peixoto and A. Abelha and J. Machado},
editor = {Ferras C. Diez J.H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187789261&doi=10.1007%2f978-3-031-54235-0_42&partnerID=40&md5=a953118bdafa8c1a1c74d6512e1723c9},
doi = {10.1007/978-3-031-54235-0_42},
issn = {23673370},
year  = {2024},
date = {2024-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {932 LNNS},
pages = {467-476},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This paper investigates the complex interrelation between economic indicators, such as inflation and government spending, and their consequences for public health and suicide rates. Scholars and policymakers are increasingly focused on understanding how economic volatility affects individuals’ resource access and stress levels, thus influencing societal well-being. This study analyzes data from three public datasets using an ETL process developed in Python. After being stored in an Apache HBase database, this data can be visualized in interactive dashboards developed in PowerBI. The data sources include the HCPI, government healthcare expenditures, and suicide rates across different time periods and locations. Key findings highlight the impact of inflation on healthcare costs, emphasizing the need for strategic healthcare planning in the face of economic fluctuations. Moreover, the research reveals intricate relationships between suicide rates and economic variables, suggesting the importance of considering mental health and social well-being in economic policies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2024 ; Conference Date: 24 January 2024 Through 26 January 2024; Conference Code:308799},
keywords = {Big data; Economics; Health care, Consumer price index; Economic indicators; Empirical analysis; Government spending; Harmonized consumer price index; Health spending; Inflation rates; Policy makers; Resource access; Suicide rate, Python},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Alves2024205,
title = {Immersive Shopping Experiences: The Role of Augmented Reality in E-Commerce},
author = {C. Alves and J. Machado},
editor = {Ferras C. Diez J.H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187788679&doi=10.1007%2f978-3-031-54256-5_19&partnerID=40&md5=a725ca93ea25b7c23887aaa3f71d2857},
doi = {10.1007/978-3-031-54256-5_19},
issn = {23673370},
year  = {2024},
date = {2024-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {933 LNNS},
pages = {205-213},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Augmented Reality (AR) is a technology that is emerging in the mobile world, more specifically on smartphones. In this sense, new mobile devices have been launched and several studies on this technology already exist. However, regarding the current era, it will still be necessary to determine the main difficulties and challenges of this technology in electronic commerce. This article aims to investigate and identify in studies with a high impact factor in the literature what the next steps could be as well as factors to mitigate difficulties in the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2024 ; Conference Date: 24 January 2024 Through 26 January 2024; Conference Code:308799},
keywords = {'current; High impact; Immersive; Impact factor; M-commerce; Smart phones, Augmented reality, Electronic commerce; Mobile telecommunication systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chaves2024195,
title = {Collaborative Platform for Intelligent Monitoring of Diabetic Foot Patients - Colab4IMDF},
author = {A. Chaves and R. Sousa and J. Machado and A. Abelha and H. Peixoto},
editor = {Ferras C. Diez J.H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187779099&doi=10.1007%2f978-3-031-54256-5_18&partnerID=40&md5=7cc7dae3a967fb508b0024598e23f8e6},
doi = {10.1007/978-3-031-54256-5_18},
issn = {23673370},
year  = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {933 LNNS},
pages = {195-204},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This research work aims to set the basis for the development of a multidisciplinary platform designed to assist healthcare professionals, their institutions and more important, diabetic patients to manage diabetic foot syndrome complications. The system’s primary function is the automated classification of diabetic foot ulcers (DFU), with the ultimate goal of expediting treatment and improving patient well-being. Given the sporadic nature of diabetic patients’ medical consultations, the time gaps between appointments can exacerbate symptoms. To address this issue, the project advocates for sustained communication between physicians and patients, facilitated by the exchange of patient images, thereby reducing the need for frequent in-person visits. This manuscript shows project’s initial phase that involves the research and modelling of an architecture that ranges from mHealth to Deep Learning algorithm capable of rapidly and accurately classifying user-submitted images, offering clinical decision support and autonomous identification of potential DFU complications from the patient’s perspective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2024 ; Conference Date: 24 January 2024 Through 26 January 2024; Conference Code:308799},
keywords = {Clinical research; Decision support systems; Deep learning; mHealth; Patient treatment, Collaborative platform; Diabetic foot; Diabetic foot ulcer; Diabetic foot wound follow-up; Diabetics patients; Follow up; Health information systems; Healthcare standards; Microservice; Multi agent, Multi agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Nguyen2024,
title = {Intelligent search system for resume and labor law},
author = {H. Nguyen and V. Pham and H. Q. Ngo and A. Huynh and B. Nguyen and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185847238&doi=10.7717%2fpeerj-cs.1786&partnerID=40&md5=07e26e014a02a4fb13dc43bbce9a4e56},
doi = {10.7717/peerj-cs.1786},
issn = {23765992},
year  = {2024},
date = {2024-01-01},
journal = {PeerJ Computer Science},
volume = {10},
publisher = {PeerJ Inc.},
abstract = {Labor and employment are important issues in social life. The demand for online job searching and searching for labor regulations in legal documents, particularly regarding the policy for unemployment benefits, is essential. Nowadays, each function has some programs for its working. However, there is no program that combines both functions. In practice, when users seek a job, they may be unemployed or want to transfer to another work. Thus, they are required to search for regulations about unemployment insurance policies and related information, as well as regulations about workers working smoothly and following labor law. Ontology is a useful technique for representing areas of practical knowledge. This article proposes an ontology-based method for solving labor and employment-related problems. First, we construct an ontology of job skills to match curriculum vitae (CV) and job descriptions (JD). In addition, an ontology for representing labor law documents is proposed to aid users in their search for legal labor law regulations. These ontologies are combined to construct the knowledge base of a job-searching and labor law-searching system. In addition, this integrated ontology is used to study several issues involving the matching of CVs and JDs and the search for labor law issues. A system for intelligent resume searching in information technology is developed using the proposed method. This system also incorporates queries pertaining to Vietnamese labor law policies regarding unemployment and healthcare benefits. The experimental results demonstrate that the method designed to assist job seekers and users searching for legal labor documents is effective. © 2024 Nguyen et al.},
note = {cited By 0},
keywords = {Employment; Insurance; Knowledge representation; Laws and legislation; Personnel; Search engines, Intelligent search systems; Job description; Knowledge-based systems; Knowledge-representation; Labor laws; Legal documents; Ontology's; Searching systems; Talent acquisition; Unemployment insurance, Ontology},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Sousa2024452,
title = {Towards a Standardized Real-Time Data Repository based on Laboratory Test Results},
author = {R. Sousa and H. Peixoto and T. Guimarães and A. Abelha and J. Machado},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183899245&doi=10.1016%2fj.procs.2023.12.233&partnerID=40&md5=92265b57dddfc7c279187e0735fa722b},
doi = {10.1016/j.procs.2023.12.233},
issn = {18770509},
year  = {2024},
date = {2024-01-01},
journal = {Procedia Computer Science},
volume = {231},
pages = {452-457},
publisher = {Elsevier B.V.},
abstract = {Healthcare facilities use huge quantities of real-time and analytical data to discover meaningful information from patient clinical lab results. Advanced analytics and machine learning algorithms help doctors identify and treat patients more accurately. Accurate models must be trained, tested, and validated with enough data. New real-time data allows healthcare practitioners to quickly and accurately analyse patient demands. Healthcare organizations can improve patient care and outcomes through knowledge discovery. The goal of this effort is to develop a real-time data repository based on patient clinical exams. This collection feeds real-time monitoring panels and machine or deep learning algorithms that forecast patient progression from clinical lab results. Integrate HL7 messages from diverse sources, preprocess them, and add them to an API-accessible data warehouse. In conclusion, the proposed method creates an international-standard data warehouse. This data warehouse can increase healthcare decision-making accuracy and efficacy when utilised with machine learning models, improving patient care and outcomes through more personalised treatment options. © 2024 The Authors. Published by Elsevier B.V.},
note = {cited By 0; Conference of 14th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 13th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023 ; Conference Date: 7 November 2023 Through 9 November 2023; Conference Code:196395},
keywords = {API; Clinical test result; Clinical tests; Data repositories; Data standards; Health data; Health data standard; Patient care; Real-time data; Real-time information systems, Data warehouses, Decision making; Deep learning; Learning algorithms; Patient treatment; Real time systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Cruz2024439,
title = {Decentralize Healthcare Marketplace},
author = {G. Cruz and T. Guimarães and M. F. Santos and J. Machado},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183845666&doi=10.1016%2fj.procs.2023.12.231&partnerID=40&md5=7930f35c92e1fa1e5b922e34be970ec1},
doi = {10.1016/j.procs.2023.12.231},
issn = {18770509},
year  = {2024},
date = {2024-01-01},
journal = {Procedia Computer Science},
volume = {231},
pages = {439-444},
publisher = {Elsevier B.V.},
abstract = {This paper presents the development of a decentralized healthcare marketplace, where patients can anonymously sell their data in the form of Non-Fungible Tokens (NFTs) to researchers. The platform aims to merge NFTs, decentralized finance, and patients' record management, empowering patients, expanding research opportunities, and enhancing healthcare outcomes through blockchain technology. The methodology employed for this research includes field research and discussions with professionals, ensuring credible and valuable insights. The article concludes that blockchain technology offers a transformative solution for healthcare record management, addressing issues of data security, privacy, interoperability, and transparency. Future work focuses on scalability and performance optimization, regulatory compliance, and governance, as well as interoperability and data standardization, among other areas, to unlock the platform's full potential. © 2024 The Authors. Published by Elsevier B.V.},
note = {cited By 0; Conference of 14th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 13th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023 ; Conference Date: 7 November 2023 Through 9 November 2023; Conference Code:196395},
keywords = {Block-chain; Decentralised; Decentralized finance; Field research; Healthcare; Healthcare record; Patient record; Record management; Research opportunities; Security/privacy, Blockchain, Commerce; Health care; Information management; Interoperability; Regulatory compliance},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Alves2024623,
title = {Review for Augmented Reality Shopping Application for Mobile Systems},
author = {C. Alves and J. Machado and J. L. Reis},
editor = {Del Rio Araujo M. dos Santos J.P. Reis J.L.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171888848&doi=10.1007%2f978-981-99-0333-7_45&partnerID=40&md5=bfb14fae4f33db950967b78bf1a04b3a},
doi = {10.1007/978-981-99-0333-7_45},
issn = {21903018},
year  = {2024},
date = {2024-01-01},
journal = {Smart Innovation, Systems and Technologies},
volume = {344},
pages = {623-634},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The evolution of e-commerce has, in recent times, made a significant advance largely due to the forced digital transformation and to Sars-cov-2. Thus, augmented reality (AR) technology is increasingly being adopted by companies either to increase brand value or to improve the shopping experience for their consumers. This work aims to present a comparative study between some shopping applications using AR, augmented reality shopping applications (ARSAs), which are carried out in stores or online. The applications were studied in articles in Open Access format and the focus was to understand what their conclusions were as well as the problems and trends of future research toward this type of technologies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
note = {cited By 0; Conference of International Conference on Marketing and Technologies, ICMarkTech 2022 ; Conference Date: 1 December 2022 Through 3 December 2022; Conference Code:294609},
keywords = {Augmented reality, Augmented reality technology; Brand values; Comparatives studies; Digital transformation; E- commerces; Mobile app; Mobile systems; OpenAccess; Shopping app; Type of technology, Mobile commerce},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Gonçalves2023,
title = {Urban Traffic Simulation Using Mobility Patterns Synthesized from Real Sensors},
author = {F. Gonçalves and G. O. Silva and A. Santos and A. M. A. C. Rocha and H. Peixoto and D. Durães and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180725294&doi=10.3390%2felectronics12244971&partnerID=40&md5=1afe50b700cdb99c864c8c3375407731},
doi = {10.3390/electronics12244971},
issn = {20799292},
year  = {2023},
date = {2023-01-01},
journal = {Electronics (Switzerland)},
volume = {12},
number = {24},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {Smart cities are an ongoing research topic with multiple sub-research areas, from traffic control to optimization and even safety. However, testing the new methodologies or technologies directly in the real world is an almost impossible feat that, inclusively, can result in disaster. Thus, there is the importance of simulation. Simulation enables testing new and complex methodologies and gauging their impact in a realistic context without adding any safety issues. Additionally, these can accurately map real-world conditions depending on the simulation configuration. One key aspect of the simulation is the traffic flows in the simulated region. These may be hard to find and, if ill-set, may introduce bias in the results. This work is on the characterization of the traffic in the city center of Guimarães, Portugal. An urban simulation scenario was established, using SUMO as the mobility traffic simulator, with traffic patterns derived from real-world data provided by Guimarães City Hall and using Eclipse MOSAIC for extended vehicular simulation. Apart from mobility patterns analysis, this work also provides publicly accessible datasets, simulations, and applications made available to future research works. © 2023 by the authors.},
note = {cited By 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Silva2023,
title = {A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications},
author = {A. L. Silva and P. Oliveira and D. Durães and D. Fernandes and R. Névoa and J. Monteiro and P. Melo-Pinto and J. Machado and P. Novais},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166008165&doi=10.3390%2fs23146427&partnerID=40&md5=53b52635577fee2589e3dd1eca23b062},
doi = {10.3390/s23146427},
issn = {14248220},
year  = {2023},
date = {2023-01-01},
journal = {Sensors},
volume = {23},
number = {14},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {The rapid development of deep learning has brought novel methodologies for 3D object detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, technologies and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it has become crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow for the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow for a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements. © 2023 by the authors.},
note = {cited By 1},
keywords = {3D object; 3d object detection; Autonomous driving; Deep learning method; Learning methods; LiDAR sensing technology; Objects detection; Sensing technology; Software Specification; State of the art, article; deep learning; software, Autonomous vehicles; Deep learning; Learning systems; Object recognition; Optical radar; Specifications; Three dimensional computer graphics, Object detection},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Montenegro2023274,
title = {AI-Based Medical Scribe to Support Clinical Consultations: A Proposed System Architecture},
author = {L. Montenegro and L. M. Gomes and J. M. Machado},
editor = {Vale Z. Moniz N. Moniz N.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180626879&doi=10.1007%2f978-3-031-49011-8_22&partnerID=40&md5=a02dc441c31b5e5b04b0b741a3aaa2bd},
doi = {10.1007/978-3-031-49011-8_22},
issn = {03029743},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {14116 LNAI},
pages = {274-285},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {AI applications in hospital frameworks can improve patient-care quality and efficient workflows and assist in digital transformation. By designing Smart Hospital infrastructures, creating an efficient framework enables patient information exchange between hospitals, point of care, and remote patient monitoring. Deep learning (DL) solutions play important roles in these infrastructures’ digital transformation process and architectural design. Literature review shows that DL solutions based on Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) are rising concerning clinical data digitalisation, population health management, and improving patient care. Nevertheless, one of the literature’s shortcomings highlights the limited research using these solutions in real-world medical environments. As part of smart hospitals, smart medical scribes have been presented in several studies as a promising solution. However, just a few studies have tested it in real settings. Moreover, it was limited to non-existent studies on non-English systems, even yet to be found similar studies for European Portuguese. The proposed study evaluates NLP-based solutions in real-life Portuguese clinical settings focused on patient care for Smart Healthcare applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 22nd EPIA Conference on Artificial Intelligence, EPIA 2023 ; Conference Date: 5 September 2023 Through 8 September 2023; Conference Code:305499},
keywords = {AI applications; Automatic speech recognition; Digital transformation; Language processing; Natural language processing; Natural languages; Patient care; Smart healthcare; Smart hospital; Systems architecture, Deep learning; Health care; Natural language processing systems; Population statistics; Speech recognition, Hospitals},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Denanti2023,
title = {The Correlation of Headline News Sentiment and Stock Return during Dividend Period},
author = {S. P. Denanti and I. Yunita and T. Widarmanti and J. M. Ferreira Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180008658&doi=10.1109%2fICONDBTM59210.2023.10327342&partnerID=40&md5=cf21fdc9bbb11f8c4a8c6e089f91285e},
doi = {10.1109/ICONDBTM59210.2023.10327342},
isbn = {9798350328028},
year  = {2023},
date = {2023-01-01},
journal = {2023 International Conference on Digital Business and Technology Management, ICONDBTM 2023},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Stock price fluctuations require investors to gather more information to make informed decisions for optimal returns. Dividend announcements are the basis for investors to make investment decisions, as they contain asymmetric information about company performance. However, during the dividend period stock prices often fluctuate, which can make it difficult for investors to make decisions. Therefore, market participants can use sentiment analysis to assess company performance and assist in making investment decisions. The purpose of this study is to analyse headline sentiment during the dividend period, and how it relates to the stock returns of companies included in the LQ45 Index from 2018 to 2022. In conducting the sentiment analysis, the FinBERT model was used to classify dividend news headlines into positive, negative, and neutral sentiment. Then, a Spearman rank correlation test is conducted with the closing price of the stock to see the relationship. The results show that the sentiment formed by news headlines is dominated by neutral sentiment (46%), followed by positive sentiment (28%) and negative sentiment (26%). The study, conducted over the 2018-2022 dividend period, shows a positive relationship between news headlines and stock returns. The analysis shows that the sentiment conveyed in news headlines has a statistically significant positive correlation with changes in company stock returns. These findings suggest that the sentiment expressed in news headlines can serve as a valuable indicator for predicting and understanding fluctuations in stock returns during dividend periods. © 2023 IEEE.},
note = {cited By 0; Conference of 2023 International Conference on Digital Business and Technology Management, ICONDBTM 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:195011},
keywords = {Asymmetric information; Company performance; Dividend period; FinBERT; Headline news; Informed decision; Investment decisions; Sentiment analysis; Stock price fluctuation; Stock returns, Costs; Financial markets; Investments, Sentiment analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Machado2023C1,
title = {Correction to: Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference (Springer Science and Business Media Deutschland GmbH, 10.1007/978-3-031-23210-7)},
author = {J. M. Machado and P. Chamoso and G. Hernández and G. Bocewicz and R. Loukanova and E. Jove and A. M. Del Rey and M. Ricca},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179169137&doi=10.1007%2f978-3-031-23210-7_23&partnerID=40&md5=0368ea9eef69697fdf55397293bd4a22},
doi = {10.1007/978-3-031-23210-7_23},
issn = {23673370},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {585},
pages = {C1},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In the original version of the book, the following belated corrections have been incorporated: The affiliation of the editor “Roussanka Loukanova” has been changed to “Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria” in the Frontmatter. The book has been updated with the changes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.},
note = {cited By 0; Conference of 19th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2022 ; Conference Date: 13 July 2022 Through 15 July 2022; Conference Code:290759},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Moya2023217,
title = {Clustering ABI Patients for a Customized Rehabilitation Process},
author = {A. Moya and L. Zhinin-Vera and E. Navarro and J. Jaen and J. Machado},
editor = {Urzaiz G. Bravo J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178644656&doi=10.1007%2f978-3-031-48642-5_21&partnerID=40&md5=5f6a0755f77fbc70a3d9148792855ca9},
doi = {10.1007/978-3-031-48642-5_21},
issn = {23673370},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {842 LNNS},
pages = {217-228},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Acquired Brain Injury (ABI) is a medical condition resulting from injury or disease that affects the functioning of the brain. The incidence of ABI has increased in recent years, highlighting the need for a comprehensive approach to treatment and rehabilitation to improve patients’ quality of life. Developing appropriate therapies for these patients is a challenging task because of the wide diversity of effects and severity they may suffer. This problem exacerbates the complexity of designing the rehabilitation activities, which is a time-consuming and complicated task that may cause poor patient recovery, if such activities are poorly designed. In order to overcome this problem, it is common practice to create groups of patients with similar complaints and deficits and to design rehabilitation activities that may be reused internally by such groups, facilitating comparative analyses. Usually, such grouping is conducted by specialists who may neglect to detect commonalities due to the huge amount of information to be processed. In this work, a clustering of ABI patients is performed following a systematic methodology, from preprocessing the data to applying appropriate clustering algorithms, in order to guarantee an adequate clustering of ABI patients. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 15th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2023 ; Conference Date: 28 November 2023 Through 29 November 2023; Conference Code:304769},
keywords = {Acquired brain injuries; Amount of information; Clusterings; Comparative analyzes; Medical conditions; Quality of life; Rehabilitation activities; Systematic methodology, Clustering algorithms; Patient treatment, Patient rehabilitation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Zhinin-Vera202315,
title = {A Reinforcement Learning Algorithm for Improving the Generation of Telerehabilitation Activities of ABI Patients},
author = {L. Zhinin-Vera and A. Moya and E. Navarro and J. Jaen and J. Machado},
editor = {Urzaiz G. Bravo J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178594918&doi=10.1007%2f978-3-031-48306-6_2&partnerID=40&md5=3823104564976273c57dde7b795187c6},
doi = {10.1007/978-3-031-48306-6_2},
issn = {23673370},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {835 LNNS},
pages = {15-26},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Acquired Brain Injury (ABI) is a condition caused by an injury or disease that disrupts the normal functioning of the brain. In recent years, there has been a significant increase in the incidence of ABI, highlighting the need for a comprehensive approach that improves the rehabilitation process and, thus, provides people with ABI with a better quality of life. Developing appropriate rehabilitation activities for these patients is a major challenge for experts in the field, as their poor design can hinder the recovery process. One way to address this problem is through the use of smart systems that generate such rehabilitation activities in an automatic way that can then be modified by therapists as they deem appropriate. This automatic generation of rehabilitation activities uses experts’ knowledge to determine their suitability according to the patient’s needs. The problem is that this knowledge may be ill-defined, hampering the rehabilitation process. This paper investigates the possibility of applying Deep Q-Networks, a Reinforcement Learning (RL) algorithm, to evolve and adapt that information according to the outcomes of the rehabilitation process of groups of patients. This will help minimize possible errors made by experts and improve the rehabilitation process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 15th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2023 ; Conference Date: 28 November 2023 Through 29 November 2023; Conference Code:304769},
keywords = {Acquired brain injuries; Condition; Deep q-network; Gesture interaction; Multi-modal; Multimodal gesture interaction; Rehabilitation activities; Reinforcement learning algorithms; Reinforcement learnings; Telerehabilitation, Brain; Deep learning; Learning algorithms; Patient rehabilitation, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Hernández2023300,
title = {A Machine Learning Approach to Evaluating the Relationship Between Dental Extraction and Craniofacial Growth in Adolescents},
author = {G. Hernández and A. González-Briones and J. Machado and P. Chamoso and P. Novais},
editor = {Bonsangue M. M. Anutariya C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177874390&doi=10.1007%2f978-981-99-7969-1_22&partnerID=40&md5=97e552640b738ab3e2668bc83b3e3e02},
doi = {10.1007/978-981-99-7969-1_22},
issn = {18650929},
year  = {2023},
date = {2023-01-01},
journal = {Communications in Computer and Information Science},
volume = {1942 CCIS},
pages = {300-313},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {There may be multiple reasons for tooth extraction, such as deep cavities, an infection that has destroyed an important portion of the tooth or the bone that surrounds it, or for orthodontic reasons, such as the lack of space for all the teeth in the mouth. In the case of orthodontics, however, there is a relationship between tooth extraction and the craniofacial morphological pattern. The purpose of this study is to establish whether such a relationship exists in adolescents and to evaluate it and to serve as a tool to support medical decision making. Machine Learning techniques can now be applied to datasets to discover relationships between different variables. Thus, this study involves the application of a series of Machine Learning techniques to a dataset containing information on orthodontic tooth extraction in adolescents. It has been discovered that by following simple rules it is possible to identify the need of treatment in 98.7% of the cases, while the remaining can be regarded as “limited cases”, in which an expert’s opinion is necessary. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.},
note = {cited By 0; Conference of 1st International Conference on Data Science and Artificial Intelligence, DSAI 2023 ; Conference Date: 27 November 2023 Through 29 November 2023; Conference Code:304419},
keywords = {Craniofacial; Craniofacial morphological growth; Deep cavity; Dental extraction; Machine learning approaches; Machine learning techniques; Machine-learning; Morphological patterns; Orthodontic treatments; Tooth extraction, Decision making; Dentistry; Learning algorithms; Machine learning, Extraction},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Dias2023288,
title = {Enhancing Data Science Interoperability: An Innovative System for Managing OpenEHR Structures},
author = {M. Dias and R. Sousa and J. Duarte and H. Peixoto and A. Abelha and J. Machado},
editor = {Bonsangue M. M. Anutariya C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177871998&doi=10.1007%2f978-981-99-7969-1_21&partnerID=40&md5=19b18d908fc002f4b8eab31c03dfc5f0},
doi = {10.1007/978-981-99-7969-1_21},
issn = {18650929},
year  = {2023},
date = {2023-01-01},
journal = {Communications in Computer and Information Science},
volume = {1942 CCIS},
pages = {288-299},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The concept of e-Health is increasingly used in the healthcare industry, referring to the development and application of software and hardware solutions for efficient collection, storage, manipulation, and communication of data, to improve healthcare delivery. Data Science Interoperability is critical to create a data repository from which to implement artificial intelligence models that support the decision-making process. The lack of interoperability in Health Information Systems (HIS) has been a significant challenge. The need for systems that promote interoperability between HIS within the same institution or even between HIS from different institutions is a worldwide concern. A system that contributes to improving interoperability in healthcare through the use of the openEHR standard is proposed with this paper. The system provides a way to manipulate clinical data by creating an artifact built with React and NextJS that allows the conversion of openEHR standardized data into a JSON object. The artifact being a web application presents a new way for users to check openEHR data, while the API can be used by developers to work with openEHR data in a more accessible and supported way. The results of this research and engineering effort have been successful in presenting a new approach to implementing yet another tool to help healthcare professionals and biomedical software engineers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.},
note = {cited By 0; Conference of 1st International Conference on Data Science and Artificial Intelligence, DSAI 2023 ; Conference Date: 27 November 2023 Through 29 November 2023; Conference Code:304419},
keywords = {Application programming interfaces (API); Application programs; Data Science; Decision making; Digital storage; Health care; Information systems; Information use, Data science interoperability; Development and applications; E health; Ehealth; Health information systems; Healthcare industry; Innovative systems; Openehr; Software and hardwares; Software solution, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Fauzan2023,
title = {Breast Cancer Detection on Histopathology Images Using Pre-trained Computer Vision Models},
author = {D. F. Fauzan and R. Fauzi and O. N. Pratiwi and J. M. Ferreira Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175636654&doi=10.1109%2fICADEIS58666.2023.10270900&partnerID=40&md5=0526f55af1d7651d110203f5f4f0b012},
doi = {10.1109/ICADEIS58666.2023.10270900},
isbn = {9798350303414},
year  = {2023},
date = {2023-01-01},
journal = {ICADEIS 2023 - International Conference on Advancement in Data Science, E-Learning and Information Systems: Data, Intelligent Systems, and the Applications for Human Life, Proceeding},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Breast cancer is the most common type of cancer worldwide. According to the World Health Organization (WHO), there were 7.8 million women alive in 2020 who had been diagnosed with breast cancer, and it has claimed more women's lives than any other kind of cancer. With the recent rise of artificial intelligence, breast cancer detection using deep learning techniques is getting more popular. However, creating a deep learning model for a specific task from scratch costs a lot of time and money. Transfer learning is a well-known method that can make deep learning developments more efficient by leveraging pre-trained models. Using the BreakHis dataset, this paper will compare three cutting-edge pre-trained computer vision models: DenseNet, RegNet, and BiT, in predicting malignant or benign tumor tissue from breast histopathology images to determine which model is better for that specific task. Although the DenseNet model achieves the highest score with 93.7% Area Under the ROC Curve (AUC) and 97.4% Average Precision Score (APS), the BiT model is more suitable for deployment in a real-world setting since it can predict more malignant cases correctly than the other two models with a sensitivity score of 90.79%. © 2023 IEEE.},
note = {cited By 0; Conference of 5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:193233},
keywords = {Breast Cancer; Breast cancer detection; Cancer detection; Deep learning; Learning models; Learning techniques; Specific tasks; Transfer learning; Vision model; World Health Organization, Computer vision, Deep learning; Diseases; Learning systems; Medical imaging; Transfer learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Arfilinia2023,
title = {Multidimensional Sentiment Analysis of Tourism Object in DKI Jakarta, Banten, East Java, Central Java and West Java using Support Vector Machine Algorithm},
author = {A. Arfilinia and R. Andreswari and F. Hamami and J. M. F. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175628715&doi=10.1109%2fICADEIS58666.2023.10270985&partnerID=40&md5=9141e7a3e64b959a43789749f4526161},
doi = {10.1109/ICADEIS58666.2023.10270985},
isbn = {9798350303414},
year  = {2023},
date = {2023-01-01},
journal = {ICADEIS 2023 - International Conference on Advancement in Data Science, E-Learning and Information Systems: Data, Intelligent Systems, and the Applications for Human Life, Proceeding},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Information technology provides several conveniences, one of which is facilitating tourists in searching for information about tourist attractions. One of the services that can be used is Google My Business. The comments or reviews from tourists are numerous, thus requiring a significant amount of time to review them one by one. Therefore, a method is needed to address this issue, which is sentiment analysis. In this study, a multidimensional sentiment analysis was conducted on tourist attractions in the provinces of DKI Jakarta, Banten, East Java, Central Java, and West Java. Data was collected between January and March 2023 using the Data Miner tool. Two labeling techniques, namely Transformer and Textblob, were compared for labeling the data. The confusion matrix was employed as the evaluation tool, and the Support Vector Machine (SVM) technique was used to implement sentiment analysis. Labeling using transformers obtains an accuracy of 0.8169 or 81.6%, then the average precision, recall and f1-score are 68%, 54%, abd 58%. While labeling using textblob, obtained an accuracy of 0.9257 or 92.5%, the average precision is 88%, recall and f1-score results are 87%. The accuracy, precision, recall, and f1-score results indicate that labeling using Textblob outperforms the labeling using Transformers. © 2023 IEEE.},
note = {cited By 0; Conference of 5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:193233},
keywords = {Confusion matrix; F1 scores; Labelings; Multi-dimensional sentiments; Multidimensional sentiment analyse; Sentiment analysis; Support vectors machine; Textblob; Transformer, Image resolution; Matrix algebra; Sentiment analysis, Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{IGustiNgurahAgungAgniPrema2023,
title = {Discovery of Hospital Billing Process in a Regional Hospital Using Process Mining},
author = {N. I Gusti Ngurah Agung Agni Prema and P. Naufal Avilandi and Fathan and R. Andreswari and J. M. F. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175616569&doi=10.1109%2fICADEIS58666.2023.10271040&partnerID=40&md5=9da901ec00a5a466f5c8da5360d3e6d7},
doi = {10.1109/ICADEIS58666.2023.10271040},
isbn = {9798350303414},
year  = {2023},
date = {2023-01-01},
journal = {ICADEIS 2023 - International Conference on Advancement in Data Science, E-Learning and Information Systems: Data, Intelligent Systems, and the Applications for Human Life, Proceeding},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Delays and complexities in the billing process for healthcare services can inconvenience patients and hinder the efficient functioning of regional hospitals. This study aims to utilize process mining techniques to analyze event logs and identify bottleneck activities within the billing process. By evaluating the process, the research aims to understand the causality behind these bottlenecks and propose effective solutions for enhancing efficiency and reducing time costs. The research employs the PM4Py open-source toolkit, including PM4Py-GPU for computationally intensive tasks. Through fitness alignments between event logs and process models, it is found that approximately 47.128 percent of traces match the process model, exhibiting a good level of conformity with an average fitness of 0.8888. Notably, the study reveals that over 3 percent of billing processes in regional hospitals exhibit repetitive occurrences of specific activities consecutively. The identification of this repetitive activity pattern prompts a deeper investigation into its root causes and implications for resource utilization and performance. By addressing these causative factors, the research aims to propose optimized approaches to streamline the billing process, thus enhancing overall efficiency and customer satisfaction levels for the hospitals. Overall, the findings of this study contribute to a comprehensive understanding of the billing process in healthcare services and provide valuable insights for hospitals to implement targeted improvements, reduce delays, and offer high-quality services to their patients. © 2023 IEEE.},
note = {cited By 0; Conference of 5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:193233},
keywords = {Celonis; Effective solution; Event logs; Healthcare services; Hospital biling; Mining techniques; PM4Py; Process mining; Process-models; Time cost, Customer satisfaction; Data mining; Efficiency; Health care, Hospitals},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chaves2023230,
title = {Intelligent Systems in Healthcare: An Architecture Proposal},
author = {A. Chaves and L. Montenegro and H. Peixoto and A. Abelha and L. Gomes and J. Machado},
editor = {Hornos M. J. Julian Inglada V. Novais P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174435986&doi=10.1007%2f978-3-031-43461-7_23&partnerID=40&md5=f1fab121626cc690c7357a2d9bd721ba},
doi = {10.1007/978-3-031-43461-7_23},
issn = {23673370},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {770 LNNS},
pages = {230-238},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Multi-Agent Systems has existed for decades and has focused on principles such as loose coupling, distribution, reactivity, and local state. Despite substantial tool and programming language research and development, industry adoption of these systems has been restricted, particularly in the healthcare arena. Artificial intelligence, on the other hand, entails developing computer systems that can execute tasks that normally require human intelligence, such as decision-making, problem-solving, and learning. The goal of this article is to develop and implement an architecture that includes multi-agent systems with microservices, leveraging the capabilities of both methodologies in order to harness the power of Artificial Intelligence in the healthcare industry. It assesses the proposed architecture’s merits and downsides, as well as its relevance to various healthcare use cases and the influence on system scalability, adaptability, and maintainability. Indeed, the proposed architecture is capable of meeting the objectives while maintaining scalability, flexibility, and adaptability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.},
note = {cited By 1; Conference of 14th International Symposium on Ambient Intelligence, ISAmI 2023 ; Conference Date: 12 July 2023 Through 14 July 2023; Conference Code:301589},
keywords = {Coupling distribution; Development industry; Intelligent healthcare; Local state; Loose couplings; Microservice; Programming language researches; Proposed architectures; Reactivity state; Research and development, Decision making; Health care; Intelligent agents; Intelligent systems; Scalability, Multi agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@book{Machado2023v,
title = {Preface},
author = {J. M. Machado and H. Peixoto},
editor = {Peixoto H. Machado J.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172778811&partnerID=40&md5=1a12cacf0beb52e52afbb5f6dd9b96fd},
issn = {18678211},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {485 LNICST},
pages = {v-vi},
publisher = {Springer Science and Business Media Deutschland GmbH},
note = {cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
@inproceedings{Neto202391,
title = {Recommendation of Medical Exams to Support Clinical Diagnosis Based on Patient’s Symptoms},
author = {C. Neto and D. Ferreira and H. Cunha and M. Pires and S. Marques and R. Sousa and J. Machado},
editor = {Peixoto H. Machado J.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172725999&doi=10.1007%2f978-3-031-38204-8_8&partnerID=40&md5=e29bde9aad6cf908bce88ef83007236a},
doi = {10.1007/978-3-031-38204-8_8},
issn = {18678211},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {485 LNICST},
pages = {91-100},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Nowadays, it is essential that the error in the decisions made by health professionals is as small as possible. This applies to any medical area, including the recommendation of medical exams based on certain symptoms for the diagnosis of diseases. This study aims to explore the use of different Machine Learning techniques to increase the confidence of the medical exams prescribed by healthcare professionals. A successful implementation of this proposal could reduce the probability of medical errors in what concerns the prescription of medical exams and, consequently, the diagnosis of medical conditions. Thus, in this paper, six Machine Learning models were applied and optimized, namely, RF, DT, k-NN, NB, SVM and RNN, in order to find the most suitable model for the problem at hand. The results obtained with this study were promising, achieving high accuracy values with RF, DT and k-NN. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
note = {cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899},
keywords = {Clinical diagnosis; CRISP-DM; Diagnoses of disease; Health care professionals; Health professionals; Machine learning techniques; Medical areas; Medical conditions; Medical errors; Medical exam, Diagnosis; Learning systems; Nearest neighbor search, Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sousa202328,
title = {The Impact of Contingency Measures on the COVID-19 Reproduction Rate},
author = {R. Sousa and D. Oliveira and F. Hak and J. Machado},
editor = {Peixoto H. Machado J.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172722736&doi=10.1007%2f978-3-031-38204-8_3&partnerID=40&md5=f2bd2a51fa043e2d5a2f974d3f33ef48},
doi = {10.1007/978-3-031-38204-8_3},
issn = {18678211},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {485 LNICST},
pages = {28-37},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The SARS-CoV-2 virus had a major impact on the health of the world’s population, causing governments to take progressively more cautious measures. All of these measures took into account the pandemic situation in the region in real time, with the aim of slowing down the spread of the infection as much as possible and reducing the associated mortality. This article aims to study the impact of preventive measures on the spread of COVID-19 and the consequent impact on excess deaths. In order to obtain the results presented, Big Data techniques were used for data storage and processing. As a result it can be concluded that Gross Domestic Product (GDP) is directly proportional to the Human Development Index (HDI), Higher GDP per capita are associated with a higher number of new cases of COVID-19 and R-index is inversely proportional to the severity of the contingency measures. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
note = {cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899},
keywords = {Big data analyse; Contigency; Contigency measure; Correlation; Covid-19; Gross domestic products; Powerbi; Preventive measures; Proliferation rate; Real- time, Big data; Cell proliferation; Data handling; Digital storage, COVID-19},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sousa202316,
title = {COVID-19 Cases and Their Impact on Global Air Traffic},
author = {R. Sousa and J. Gomes and J. Gomes and M. Arcipreste and P. Guimarães and D. Oliveira and J. Machado},
editor = {Peixoto H. Machado J.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172721784&doi=10.1007%2f978-3-031-38204-8_2&partnerID=40&md5=64a4cb6986ae3fc0935b9c82e33c5145},
doi = {10.1007/978-3-031-38204-8_2},
issn = {18678211},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {485 LNICST},
pages = {16-27},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The air transport industry has marked unprecedented changes throughout the pandemic period of Covid-19 infection. Mostly in the number of flights canceled, liquidation of airlines and disconnection between points worldwide. The existing documentation relating to air traffic, in the specific period of this study, can be extracted, processed and visualized through tools widely used to support case study assumptions, especially in the context of Big Data. This document addresses to the use of a Big Data architecture to survey, analyze and explore different data sources and consequent loading, transformation and visual representation of the results obtained in order to verify the impact of the number of cases of infection by Covid-19 in air traffic. Based on the results obtained through the described methodology, it can be stated that the number of cases of infection by Covid-19 presents a significant impact on the number of flights that occurred ever since (around 50% less flights). © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
note = {cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899},
keywords = {Air traffics; Air transport industry; Big data architecture; Case-studies; Covid-19 world impact; Data architectures; Data-source; GDP; Global air traffic; Survey analysis, Air transportation; Aviation; COVID-19; Metadata, Big data},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ramos2023351,
title = {Information Technology Monitoring in Healthcare: A Case Study},
author = {V. Ramos and C. Marques and H. Peixoto and J. Machado},
editor = {Ibarra W. Ferras C. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172692659&doi=10.1007%2f978-3-031-33261-6_30&partnerID=40&md5=b17b14aec696139f74f78283fdc0660d},
doi = {10.1007/978-3-031-33261-6_30},
issn = {23673370},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {692 LNNS},
pages = {351-361},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The healthcare environment is particularly relevant when discussing information technology infrastructure monitoring since availability and communication are vital for the proper functioning of healthcare units. It is important to be able to easily monitor and observe each unit from a single point of access so that actions can be swiftly taken when there is a problem. This paper proposes a multi-site and multi-organization web and microservices-based information technology infrastructure monitoring solution. In addition to exploring the developed system and its architecture, it presents a case study resulting from the system’s implementation in an organization and holds a discussion about the obtained results to determine whether a multi-platform monitoring system improves information technology availability in the healthcare industry. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2023 ; Conference Date: 24 April 2023 Through 26 April 2023; Conference Code:298099},
keywords = {Case-studies; Health information systems; Healthcare; Healthcare environments; Information technology infrastructure; Infrastructure monitoring; IT infrastructure monitoring; IT infrastructures; Microservice; Technology monitoring, Health care, Information systems; Information use},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Coelho2023673,
title = {Multi-agent System for Multimodal Machine Learning Object Detection},
author = {E. Coelho and N. Pimenta and H. Peixoto and D. Durães and P. Melo-Pinto and V. Alves and L. Bandeira and J. Machado and P. Novais},
editor = {Martinez de Pison F. J. Perez Garcia H. Garcia Bringas P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172230717&doi=10.1007%2f978-3-031-40725-3_57&partnerID=40&md5=79d45a455c79da96394a5b0e05a56908},
doi = {10.1007/978-3-031-40725-3_57},
issn = {03029743},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {14001 LNAI},
pages = {673-681},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Multi-agent systems have shown great promise in addressing complex problems that traditional single-agent approaches are not be able to handle. In this article, we propose a multi-agent system for the conception of a multimodal machine learning problem on edge devices. Our architecture leverages docker containers to encapsulate knowledge in the form of models and processes, enabling easy management of the system. Communication between agents is facilitated by Message Queuing Telemetry Transport, a lightweight messaging protocol ideal for Internet of Things and edge computing environments. Additionally, we highlight the significance of object detection in our proposed system, which is a crucial component of many multimodal machine learning tasks, by enabling the identification and localization of objects within diverse data modalities. In this manuscript an overall architecture description is performed, discussing the role of each agent and the communication protocol between them. The proposed system offers a general approach to multimodal machine learning problems on edge devices, demonstrating the advantages of multi-agent systems in handling complex and dynamic environments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of Proceedings of the 18th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2023 ; Conference Date: 5 September 2023 Through 7 September 2023; Conference Code:299919},
keywords = {Agent approach; Complex problems; Learning objects; Machine learning problem; Machine-learning; Multi-modal; Multi-modality; Multimodal machine learning; Objects detection; Single-agent, Internet protocols; Machine learning; Network architecture; Object detection; Object recognition, Multi agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miranda202397,
title = {Smart Cities Using Crowdsensing and Geoferenced Notifications},
author = {R. Miranda and E. Ribeiro and D. Durães and H. Peixoto and R. Machado and A. Abelha and J. Machado},
editor = {Cardona O. Isaza G. Castillo Ossa L.F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172225087&doi=10.1007%2f978-3-031-36957-5_9&partnerID=40&md5=6e2f6d526f51ff74d61fc27085a1bf3a},
doi = {10.1007/978-3-031-36957-5_9},
issn = {23673370},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {732 LNNS},
pages = {97-110},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {As the internet and the Internet of Things continue to expand, the idea of Smart Cities has begun to take hold. Smart Cities use connected devices and data to improve the environment and quality of life of their citizens. Technologies such as crowdsensing and geofencing allow citizens to contribute to initiatives and receive notifications when near areas of interest, respectively. This paper presents a systematic review of past works on the implementation of crowdsensing and geofencing technologies in Smart Cities, with the goal of identifying their purpose, strategies, and tools. The review examines seventeen relevant papers identified through the Scopus citation and abstract database. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 1; Conference of 2nd International Conference on Sustainable Smart Cities and Territories, SSCT 2023 ; Conference Date: 21 June 2023 Through 23 June 2023; Conference Code:300219},
keywords = {Area of interest; Crowdsensing; Geofence; Location based; Location-based notification; Quality of life; Smart notification; Strategies and tools; Systematic Review, Crowdsourcing, Internet of things; Smart city},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sousa2023347,
title = {Implementing a Software-as-a-Service Strategy in Healthcare Workflows},
author = {R. Sousa and H. Peixoto and A. Abelha and J. Machado},
editor = {Analide C. Sitek P. Ossowski S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172215447&doi=10.1007%2f978-3-031-38333-5_35&partnerID=40&md5=65351fd6a4cb540be0182fe1e9567940},
doi = {10.1007/978-3-031-38333-5_35},
issn = {23673370},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {740 LNNS},
pages = {347-356},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The spread of healthcare technology has resulted in a massive amount of data, particularly in the form of laboratory test results, which play an important role in medical diagnosis and treatment. However, managing and interpreting such large amounts of data has proven increasingly difficult, particularly for resource-constrained healthcare facilities. To address this issue, we present a multi-agent system for effective laboratory test result management based on Software-as-a-Service (SaaS) technology. This paper contains a case study that evaluates the system’s performance and efficacy. The study’s goal is to examine the viability of using a multi-agent system and SaaS technology to manage laboratory test data, highlighting the system’s advantages over conventional alternatives. In the age of big data, the deployment of this system could dramatically improve healthcare service efficiency, quality, and cost-effectiveness. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 20th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2023 ; Conference Date: 12 July 2023 Through 14 July 2023; Conference Code:298499},
keywords = {Big data; Cost effectiveness; Diagnosis; Health care; Information management; Real time systems; Software agents; Software as a service (SaaS); Software testing; Web services, Case-studies; Cloud paradigm; Healthcare facility; Healthcare technology; Healthcare workflow; Laboratory test; Large amounts of data; Real-time information systems; Service strategy; Software-as-a- Service (SaaS), Multi agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Vaz2023850,
title = {Enhancing Clinical Management of Bariatric Surgery Using Business Intelligence},
author = {L. Vaz and H. Peixoto and J. Duarte and C. Alvarez and J. Machado},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164522803&doi=10.1016%2fj.procs.2023.03.114&partnerID=40&md5=33c5a7e1ee9afa79be7d332a07083b79},
doi = {10.1016/j.procs.2023.03.114},
issn = {18770509},
year  = {2023},
date = {2023-01-01},
journal = {Procedia Computer Science},
volume = {220},
pages = {850-855},
publisher = {Elsevier B.V.},
abstract = {There is a problem with collecting information in healthcare services as it is scattered among various sources. This leads to potential impact on patient care focus. To address this issue, a Business Intelligence platform was developed and implemented at the Centre for Surgical Treatment of Obesity at Centro Hospitalar do Tâmega e Sousa. The platform developed enables knowledge extraction and aids healthcare professionals to easily access helpful information and perform better decisions, specifically in regards to the growing global concern of obesity and the increasing prevalence of bariatric surgery. © 2023 Elsevier B.V.. All rights reserved.},
note = {cited By 0; Conference of 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference Date: 15 March 2023 Through 17 March 2023; Conference Code:189712},
keywords = {Bariatric surgery; Business Intelligence platform; Business-intelligence; Clinical management; Healthcare services; Knowledge extraction; Obesity; Patient care; Potential impacts; Surgical treatment, Extraction, Information analysis; Nutrition; Surgery},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miranda2023826,
title = {Data Platforms for Real-time Insights in Healthcare: Systematic Review},
author = {R. Miranda and C. Alves and A. Abelha and J. Machado},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164491019&doi=10.1016%2fj.procs.2023.03.110&partnerID=40&md5=a066521d09ece0ce9d718e34c451fe31},
doi = {10.1016/j.procs.2023.03.110},
issn = {18770509},
year  = {2023},
date = {2023-01-01},
journal = {Procedia Computer Science},
volume = {220},
pages = {826-831},
publisher = {Elsevier B.V.},
abstract = {The ever-growing usage and popularity of Internet of Things devices, coupled with Big Data technologies and machine learning algorithms, have allowed for data engineers to explore new opportunities in healthcare and continuous care. Furthermore, there is a need to reduce the gap on time from when information is created to when actions and insights can be offered. However, a challenge in implementing a large-scale data processing architecture is deciding which tools are appropriate, and how to apply them in the best way possible. For example, streaming systems are now mature enough that hospitals worldwide can use their extremely large datasets, along with data producers, to predict and influence future events. Thus, the main objective of this systematic review is to identify the state-of-the-art in data platforms on healthcare that allow the creation of metrics and actions in real-time. The PRISMA guideline for reporting systematic reviews was implemented to deliver a transparent and consistent report, validating the technological advances in a critical sector. Multiple pertinent articles and papers were retrieved from the SCOPUS abstract and citation database on May 13, 2022, using several relevant keywords to identify potentially relevant documents published from January 2020 onward. These documents must have already been published in English and been already published, and accessible through the B-ON consortium that allows Portuguese students to legally download from most publishers. Over seven studies have been selected for deeper discussion based on their relevance and impact for this review, showcasing their main objectives, data sources, and tools used, as well as their approaches for interoperability and support of machine learning algorithms for decision support. In closing, the collected articles have shown that while Big Data is currently in use at health institutions of all sizes, the ability of processing large amounts of data from sensors and events, and notifying stakeholders as quickly as possible is still in its infancy. © 2023 Elsevier B.V.. All rights reserved.},
note = {cited By 0; Conference of 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference Date: 15 March 2023 Through 17 March 2023; Conference Code:189712},
keywords = {Business-intelligence; Data engineering; Data lake; Data mining event-driven microservice; Data platform; Event-driven; Machine-learning; Real- time; Streaming systems; Systematic Review, Computer architecture; Data Analytics; Data handling; Decision support systems; Health care; Large dataset; Learning algorithms; Learning systems; Machine learning; Real time systems, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Braga2023820,
title = {An Architecture Proposal for Noncommunicable Diseases Prevention},
author = {D. Braga and D. Oliveira and R. Rosario and P. Novais and J. Machado},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164473680&doi=10.1016%2fj.procs.2023.03.109&partnerID=40&md5=befd9725885c6b3009c00b6c112ad1ff},
doi = {10.1016/j.procs.2023.03.109},
issn = {18770509},
year  = {2023},
date = {2023-01-01},
journal = {Procedia Computer Science},
volume = {220},
pages = {820-825},
publisher = {Elsevier B.V.},
abstract = {Noncommunicable Diseases (NCDs) are a leading global health challenge, causing 41 million deaths per year. Risk factors include genetics, environmental factors, and lifestyle choices. Adopting healthy lifestyles can prevent or delay the onset of NCDs, but health misinformation can lead people to make poor decisions about their health. To combat this, it is proposed to develop an Intelligent System using Artificial Intelligence techniques to collect and analyze data from social media about health topics to combat misinformation in public health and forecast NCDs, providing guidelines to prevent their spread. Methods: A systems overall architecture is presented. An innovative and novel solution that addresses the spread of information concerning health and NCDs contributes to inform public policies and infodemic management strategies. © 2023 Elsevier B.V.. All rights reserved.},
note = {cited By 0; Conference of 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference Date: 15 March 2023 Through 17 March 2023; Conference Code:189712},
keywords = {Disease prevention; Environmental factors; Global health; Healthy lifestyles; Lifestyle choices; Misinformation; Non-communicable disease; Online social listening; Prevention; Risk factors, Information management; Social networking (online), Intelligent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva2023844,
title = {A Data Acquisition and Consolidation System based on openEHR applied to Physical Medicine and Rehabilitation},
author = {I. Silva and D. Ferreira and H. Peixoto and J. Machado},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164453711&doi=10.1016%2fj.procs.2023.03.113&partnerID=40&md5=05a9e82d67ef672349732c517afce7de},
doi = {10.1016/j.procs.2023.03.113},
issn = {18770509},
year  = {2023},
date = {2023-01-01},
journal = {Procedia Computer Science},
volume = {220},
pages = {844-849},
publisher = {Elsevier B.V.},
abstract = {Shoulder pathologies are prevalent and reduce the quality of life. Due to the shoulder joint's complexity, healthcare professionals face challenges to evaluate, diagnose, and treat these pathologies. On the other hand, the acquisition, presentation, and analysis of patient data in healthcare is complex and hindered by the absence of standardization and interoperability in Database Management Systems. Hence, in this study, we propose a web platform based on openEHR structures that incorporates user interface forms for registering patient physical examinations. The benefits of implementing this approach include structured and standardized data collection, and better communication and information exchange between different systems. © 2023 Elsevier B.V.. All rights reserved.},
note = {cited By 0; Conference of 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference Date: 15 March 2023 Through 17 March 2023; Conference Code:189712},
keywords = {Data acquisition; eHealth; Hospital data processing; Information management; Medical applications; Medical computing; Pathology; User interfaces, Electronic health; Electronic health record; Health care professionals; Health records; OpenEHR; Patient data; Physical medicine; Quality of life; Shoulder joints; Shoulder pathology, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lopes2023297,
title = {Big Data in Healthcare Institutions: An Architecture Proposal},
author = {J. Lopes and R. Sousa and A. Abelha and J. Machado},
editor = {Zeng D. Huang H. Hou R.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163403837&doi=10.1007%2f978-3-031-33614-0_20&partnerID=40&md5=32838fc94653d1c8bf46169b58d98913},
doi = {10.1007/978-3-031-33614-0_20},
issn = {18678211},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {480 LNICST},
pages = {297-311},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Healthcare institutions are complex organizations dedicated to providing care to the population. Continuous improvement has made the care provided a factor of excellence in the population, improving people’s daily lives and increasing average life expectancy. Even so, the resulting aging has caused patterns to increase day by day and the paradigm of medicine to shift from reaction to prevention. Often, the principle of evidence-based medicine is compromised by lack of evidence on pathogenic mechanisms, risk prediction, lack of resources, and effective therapeutic strategies. This is even more evident in pandemic situations. The current data management tools (centered in a single machine) do not have an ideal behavior for the processing of large amounts of information. This fact combined with the lack of sensitivity for the health area makes it imminent the need to create and implement an architecture that performs this management and processing effectively. In this sense, this paper aims to study the problem of knowledge construction from Big Data in health institutions. The main goal is to present an architecture that deals with the adversities of the big data universe when applied to health. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
note = {cited By 0; Conference of 11th and 12th EAI International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022 ; Conference Date: 10 December 2022 Through 11 December 2022; Conference Code:295539},
keywords = {Architecture; Computer architecture; Health care; Information management; Information use; Medical information systems; Real time systems, Average life expectancy; Continuous improvements; Daily lives; Evidence-based medicine; Healthcare information system; Healthcare institutions; Pathogenic mechanisms; Real-time information systems; Risk predictions; Systems architecture, Big data},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Machado2023,
title = {Drug–drug interaction extraction-based system: An natural language processing approach},
author = {J. Machado and C. Rodrigues and R. Sousa and L. M. Gomes},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160305432&doi=10.1111%2fexsy.13303&partnerID=40&md5=3fd3843abb2c89d5d1db4e66c07497e9},
doi = {10.1111/exsy.13303},
issn = {02664720},
year  = {2023},
date = {2023-01-01},
journal = {Expert Systems},
publisher = {John Wiley and Sons Inc},
abstract = {Poly-medicated patients, especially those over 65, have increased. Multiple drug use and inappropriate prescribing increase drug–drug interactions, adverse drug reactions, morbidity, and mortality. This issue was addressed with recommendation systems. Health professionals have not followed these systems due to their poor alert quality and incomplete databases. Recent research shows a growing interest in using Text Mining via NLP to extract drug–drug interactions from unstructured data sources to support clinical prescribing decisions. NLP text mining and machine learning classifier training for drug relation extraction were used in this process. In this context, the proposed solution allows to develop an extraction system for drug–drug interactions from unstructured data sources. The system produces structured information, which can be inserted into a database that contains information acquired from three different data sources. The architecture outlined for the drug–drug interaction extraction system is capable of receiving unstructured text, identifying drug entities sentence by sentence, and determining whether or not there are interactions between them. © 2023 The Authors. Expert Systems published by John Wiley & Sons Ltd.},
note = {cited By 2},
keywords = {Clinical research; Data mining; Drug interactions; Expert systems; Learning algorithms; Natural language processing systems, Data-source; Drug-drug interactions; Information extraction; Interaction extraction; Language processing; Machine-learning; Natural language processing; Natural languages; Text-mining; Unstructured data, Machine learning},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Sousa2023195,
title = {Prediction Models Applied to Lung Cancer Using Data Mining},
author = {R. Sousa and R. Sousa and H. Peixoto and J. Machado},
editor = {Badica C. Jander K. Braubach L.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159416696&doi=10.1007%2f978-3-031-29104-3_22&partnerID=40&md5=7380c3a23a29e62b89ad3a27e67b79ae},
doi = {10.1007/978-3-031-29104-3_22},
issn = {1860949X},
year  = {2023},
date = {2023-01-01},
journal = {Studies in Computational Intelligence},
volume = {1089 SCI},
pages = {195-200},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Lung cancer is the most common cause of cancer death in men and the second leading cause of cancer death in women worldwide. Even though early detection of cancer can aid in the complete cure of the disease, the demand for techniques to detect the occurrence of cancer nodules at an early stage is increasing. Its cure rate and prediction are primarily dependent on early disease detection and diagnosis. Knowledge discovery and data mining have numerous applications in the business and scientific domains that provide useful information in healthcare systems. Therefore, the present work aimed to compare several prediction models as well as the features to be used, with the help of Weka and RapidMiner tools. Both classification and association rules techniques were implemented. The results obtained were quite satisfactory, with emphasis on the Naive Bayes model, which obtained an accuracy of 95.03% for cross-validation 10 folds and 94.59% for percentage split 66%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 15th International Symposium on Intelligent Distributed Computing, IDC 2022 ; Conference Date: 14 September 2022 Through 16 September 2022; Conference Code:293309},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@book{Machado2023vb,
title = {Preface},
author = {J. M. Machado and P. Chamoso and G. Hernández and G. Bocewicz and R. Loukanova and E. Jove and A. M. Del Rey and M. Ricca},
editor = {Hernandez G. Chamoso P. Machado J.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149705711&partnerID=40&md5=25649a1e8950722ea6141452766c876b},
issn = {23673370},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {585 LNNS},
pages = {v-vi},
publisher = {Springer Science and Business Media Deutschland GmbH},
note = {cited By 0; Conference of 19th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2022 ; Conference Date: 13 July 2022 Through 15 July 2022; Conference Code:290759},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
@inproceedings{Pimenta2023197,
title = {A Comprehensive Study on Personal and Medical Information to Predict Diabetes},
author = {N. Pimenta and R. Sousa and H. Peixoto and J. Machado},
editor = {Sitek P. Mehmood R. Omatu S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144976640&doi=10.1007%2f978-3-031-20859-1_20&partnerID=40&md5=a454129222acd9990e3dcc663066b957},
doi = {10.1007/978-3-031-20859-1_20},
issn = {23673370},
year  = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {583 LNNS},
pages = {197-207},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Diabetes mellitus is without a doubt one of the most well-known and prevalent diseases in people’s daily lives. Creating a tool that can predict the disease would benefit professionals and healthcare systems alike, benefiting both families and countries’ economies in general. Data Mining can be a useful factor in the development of this predictive tool. Data was explored in this study in order to determine which attributes, techniques, and approaches can effectively improve this predictive objective. The main approaches to investigating the data using CRISP-DM were classification and association rules, a methodology that allows searching and finding hidden patterns and relations within data. Results obtained and represented show sensitivity and accuracy values higher than 70%, using J48 and SVM classification algorithms, and allowed to examine that social-economical attributes are not enough to illness prediction. The same applies when only those most indicative characteristics are used—i.e. physical activity, healthy eating and lifestyle, regular health exams—which indicates that a greater set of information is needed so as to be designed an effective model. The best results were obtained using J48 and SVM classification techniques. This can be considered a step towards discovering major indicators of diabetes mellitus and the development of models capable of predicting its presence, enable the creation decision support systems that can improve professionals’ response when dealing with the disease. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 1; Conference of 19th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2022 ; Conference Date: 13 July 2022 Through 15 July 2022; Conference Code:287829},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neto2022119,
title = {Prediction Models for Coronary Heart Disease},
author = {C. Neto and D. Ferreira and J. Ramos and S. Cruz and J. Oliveira and A. Abelha and J. Machado},
editor = {Yigitcanlar T. Omatu S. Matsui K.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115256287&doi=10.1007%2f978-3-030-86261-9_12&partnerID=40&md5=bc7086cdc28db5a781d5729387dd7f0c},
doi = {10.1007/978-3-030-86261-9_12},
issn = {23673370},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {327 LNNS},
pages = {119-128},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In the current days, it is known that a great amount of effort is being applied to improving healthcare with the use of Artificial Intelligence technologies in order to assist healthcare professionals in the decision-making process. One of the most important field in healthcare diagnoses is the identification of Coronary Heart Disease since it has a high mortality rate worldwide. This disease occurs when the heart’s arteries are incapable of providing enough oxygen-rich blood to the heart. Thus, this study attempts to develop Data Mining models, using Machine Learning algorithms, capable of predicting, based on patients’ data, if a patient is at risk of developing any kind of Coronary Heart Disease within the next 10 years. To achieve this goal, the study was conducted by the CRISP-DM methodology and using the RapidMiner software. The best model was obtained using the Decision Tree algorithm and with Cross-Validation as the sampling method, obtaining an accuracy of 0.884, an AUC value of 0.942 and an F1-Score of 0.881. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 1; Conference of 18th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2021 ; Conference Date: 6 October 2021 Through 8 October 2021; Conference Code:264809},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Oliveira2022,
title = {Prediction of COVID-19 diagnosis based on openEHR artefacts},
author = {D. Oliveira and D. Ferreira and N. Abreu and P. Leuschner and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134570861&doi=10.1038%2fs41598-022-15968-z&partnerID=40&md5=7441a4e9b14a5a3237614f75f8dee800},
doi = {10.1038/s41598-022-15968-z},
issn = {20452322},
year  = {2022},
date = {2022-01-01},
journal = {Scientific Reports},
volume = {12},
number = {1},
publisher = {Nature Research},
abstract = {Nowadays, we are facing the worldwide pandemic caused by COVID-19. The complexity and momentum of monitoring patients infected with this virus calls for the usage of agile and scalable data structure methodologies. OpenEHR is a healthcare standard that is attracting a lot of attention in recent years due to its comprehensive and robust architecture. The importance of an open, standardized and adaptable approach to clinical data lies in extracting value to generate useful knowledge that really can help healthcare professionals make an assertive decision. This importance is even more accentuated when facing a pandemic context. Thus, in this study, a system for tracking symptoms and health conditions of suspected or confirmed SARS-CoV-2 patients from a Portuguese hospital was developed using openEHR. All data on the evolutionary status of patients in home care as well as the results of their COVID-19 test were used to train different ML algorithms, with the aim of developing a predictive model capable of identifying COVID-19 infections according to the severity of symptoms identified by patients. The CRISP-DM methodology was used to conduct this research. The results obtained were promising, with the best model achieving an accuracy of 96.25%, a precision of 99.91%, a sensitivity of 92.58%, a specificity of 99.92%, and an AUC of 0.963, using the Decision Tree algorithm and the Split Validation method. Hence, in the future, after further testing, the predictive model could be implemented in clinical decision support systems. © 2022, The Author(s).},
note = {cited By 6},
keywords = {artifact; diagnosis; human; pandemic, Artifacts; COVID-19; COVID-19 Testing; Humans; Pandemics; SARS-CoV-2},
pubstate = {published},
tppubtype = {article}
}
@article{Montenegro2022,
title = {Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning},
author = {L. Montenegro and M. Abreu and A. Fred and J. M. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136945525&doi=10.3390%2fapp12157404&partnerID=40&md5=eb9774a0f48977fdf747e19a68de51a4},
doi = {10.3390/app12157404},
issn = {20763417},
year  = {2022},
date = {2022-01-01},
journal = {Applied Sciences (Switzerland)},
volume = {12},
number = {15},
publisher = {MDPI},
abstract = {The success of arrhythmia classification tasks with Machine Learning (ML) algorithms is based on the handcrafting extraction of features from Electrocardiography (ECG) signals. However, feature extraction is a time-consuming trial-and-error approach. Deep Neural Network (DNN) algorithms bypass the process of handcrafting feature extraction since the algorithm extracts the features automatically in their hidden layers. However, it is important to have access to a balanced dataset for algorithm training. In this exploratory research study, we will compare the evaluation metrics among Convolutional Neural Networks (1D-CNN) and Support Vector Machines (SVM) using a dataset based on the merged public ECG signals database TNMG and CINC17 databases. Results: Both algorithms showed good performance using the new, merged ECG database. For evaluation metrics, the 1D-CNN algorithm has a precision of 93.04%, an accuracy of 93.07%, a recall of 93.20%, and an F1-score of 93.05%. The SVM classifier ((Formula presented.) = 10},
note = {cited By 6},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Ferreira2022,
title = {Predicting the Survival of Primary Biliary Cholangitis Patients},
author = {D. Ferreira and C. Neto and J. Lopes and J. Duarte and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136584059&doi=10.3390%2fapp12168043&partnerID=40&md5=abd92390befad43e20101e8e44a851a9},
doi = {10.3390/app12168043},
issn = {20763417},
year  = {2022},
date = {2022-01-01},
journal = {Applied Sciences (Switzerland)},
volume = {12},
number = {16},
publisher = {MDPI},
abstract = {Primary Biliary Cholangitis, which is thought to be caused by a combination of genetic and environmental factors, is a slow-growing chronic autoimmune disease in which the human body’s immune system attacks healthy cells and tissues and gradually destroys the bile ducts in the liver. A reliable diagnosis of this clinical condition, followed by appropriate intervention measures, can slow the damage to the liver and prevent further complications, especially in the early stages. Hence, the focus of this study is to compare different classification Data Mining techniques, using clinical and demographic data, in an attempt to predict whether or not a Primary Biliary Cholangitis patient will survive. Data from 418 patients with Primary Biliary Cholangitis, following the Mayo Clinic’s research between 1974 and 1984, were used to predict patient survival or non-survival using the Cross Industry Standard Process for Data Mining methodology. Different classification techniques were applied during this process, more specifically, Decision Tree, Random Tree, Random Forest, and Naïve Bayes. The model with the best performance used the Random Forest classifier and Split Validation with a ratio of 0.8, yielding values greater than 93% in all evaluation metrics. With further testing, this model may provide benefits in terms of medical decision support. © 2022 by the authors.},
note = {cited By 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Brito20221269,
title = {A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis},
author = {C. Brito and M. Esteves and H. Peixoto and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060728770&doi=10.1007%2fs11276-018-01905-4&partnerID=40&md5=9a8c346b171c728d75182e271555b144},
doi = {10.1007/s11276-018-01905-4},
issn = {10220038},
year  = {2022},
date = {2022-01-01},
journal = {Wireless Networks},
volume = {28},
number = {3},
pages = {1269-1277},
publisher = {Springer},
abstract = {Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients’ health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.},
note = {cited By 6},
keywords = {Chronic kidney disease; Classification algorithm; Clinical decision support systems; Knowledge extraction; Peritoneal dialysis; Serum creatinine; Weka, Data mining, Decision making; Decision support systems; Dialysis; Patient treatment},
pubstate = {published},
tppubtype = {article}
}
@article{Esteves20221279,
title = {The development of a pervasive Web application to alert patients based on business intelligence clinical indicators: a case study in a health institution},
author = {M. Esteves and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060693648&doi=10.1007%2fs11276-018-01911-6&partnerID=40&md5=820eb2dfb7c55bb1401f12834f41cd87},
doi = {10.1007/s11276-018-01911-6},
issn = {10220038},
year  = {2022},
date = {2022-01-01},
journal = {Wireless Networks},
volume = {28},
number = {3},
pages = {1279-1285},
publisher = {Springer},
abstract = {This paper proposes the development of a pervasive Web application based on business intelligence clinical indicators created with the data stored into the health information systems of a Portuguese health institution in the last 3 years i.e. between the beginning of 2015 and the end of 2017. With this computational tool, it is principally intended to reduce the number of appointments, surgeries, and medical examinations that were not carried out in the hospital most likely due to forgetfulness since most patients who attend this health institution are elderly people and memory loss is very common with increasing age. Therefore, patients and/or their caregivers and family members are alerted via SMS in advance and appropriately by health professionals through the Web application. This alternative is cheaper, faster, and more customizable than sending those SMS using a smartphone. Advantages liked with the use of this solution also include decreasing losses concerning time, human resources, and money. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.},
note = {cited By 4},
keywords = {Caregivers; Computational tools; Elderly people; Health information systems; Health informations; Health professionals; Pervasive webs; WEB application, Competitive intelligence; Data warehouses; Diagnosis; mHealth, Information analysis},
pubstate = {published},
tppubtype = {article}
}
@article{Reinolds2022,
title = {Deep Learning for Activity Recognition Using Audio and Video},
author = {F. Reinolds and C. Neto and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126055726&doi=10.3390%2felectronics11050782&partnerID=40&md5=ac9c7d9561a258a6337a467217fb3f91},
doi = {10.3390/electronics11050782},
issn = {20799292},
year  = {2022},
date = {2022-01-01},
journal = {Electronics (Switzerland)},
volume = {11},
number = {5},
publisher = {MDPI},
abstract = {Neural networks have established themselves as powerhouses in what concerns several types of detection, ranging from human activities to their emotions. Several types of analysis exist, and the most popular and successful is video. However, there are other kinds of analysis, which, despite not being used as often, are still promising. In this article, a comparison between audio and video analysis is drawn in an attempt to classify violence detection in real-time streams. This study, which followed the CRISP-DM methodology, made use of several models available through PyTorch in order to test a diverse set of models and achieve robust results. The results obtained proved why video analysis has such prevalence, with the video classification handily outperforming its audio classification counterpart. Whilst the audio models attained on average 76% accuracy, video models secured average scores of 89%, showing a significant difference in performance. This study concluded that the applied methods are quite promising in detecting violence, using both audio and video. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.},
note = {cited By 10},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Santos2022,
title = {Weakness Evaluation on In-Vehicle Violence Detection: An Assessment of X3D, C2D and I3D against FGSM and PGD},
author = {F. Santos and D. Durães and F. S. Marcondes and N. Hammerschmidt and J. Machado and P. Novais},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126008234&doi=10.3390%2felectronics11060852&partnerID=40&md5=86863d4a6ac270752b07826386ee8cca},
doi = {10.3390/electronics11060852},
issn = {20799292},
year  = {2022},
date = {2022-01-01},
journal = {Electronics (Switzerland)},
volume = {11},
number = {6},
publisher = {MDPI},
abstract = {When constructing a deep learning model for recognizing violence inside a vehicle, it is crucial to consider several aspects. One aspect is the computational limitations, and the other is the deep learning model architecture chosen. Nevertheless, to choose the best deep learning model, it is necessary to test and evaluate the model against adversarial attacks. This paper presented three different architecture models for violence recognition inside a vehicle. These model architectures were evaluated based on adversarial attacks and interpretability methods. An analysis of the model’s convergence was conducted, followed by adversarial robustness for each model and a sanity-check based on interpretability analysis. It compared a standard evaluation for training and testing data samples with the adversarial attacks techniques. These two levels of analysis are essential to verify model weakness and sensibility regarding the complete video and in a frame-by-frame way. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.},
note = {cited By 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Miranda2022,
title = {Machine Learning Applied to Health Information Exchange},
author = {F. Miranda and A. R. Sousa and J. Duarte and A. C. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153941029&doi=10.4018%2fijrqeh.298634&partnerID=40&md5=161bc016c74c5f111373936f70723343},
doi = {10.4018/ijrqeh.298634},
issn = {21609551},
year  = {2022},
date = {2022-01-01},
journal = {International Journal of Reliable and Quality E-Healthcare},
volume = {11},
number = {1},
publisher = {IGI Global},
abstract = {The interest in artificial intelligence (AI) has grown in the last few years. The healthcare community is no exception. The present work is focused on the exchange of medical information, using the Health Level Seven (HL7) international standards. The main objective of the present work is to develop an AI model capable of inferring if for a given hour exists a peak in the number of exchanged messages. To accomplish that, two different deep learning models were created, an artificial neural networks (ANN) and long short-term memory (LSTM). The intention is to observe which is capable to perceive the situation better considering the environment and features of a healthcare facility. Using laboratory-generated data, it was possible to simulate variations and differences in “traffic.” Comparing the LSTM vs. ANN model, the first is capable of outputting peaks better but for considered mean values that do not happen. For the given context, predicting the peak is essential, so the LSTM is the right choice and uses fewer features that are good regarding performance. © The Author(s) 2022.},
note = {cited By 1},
keywords = {article; artificial intelligence; artificial neural network; deep learning; health care facility; health level 7; machine learning; medical information system; short term memory; standard},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Trisuciana2022,
title = {Clustering of COVID-19 Vaccination Recipients in DKI Jakarta Using the K-Medoids Algorithm},
author = {F. M. Trisuciana and D. Witarsyah and E. Sutoyo and J. M. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148897632&doi=10.1109%2fICADEIS56544.2022.10037509&partnerID=40&md5=661310bce5d9f9c229e0e64542e79a81},
doi = {10.1109/ICADEIS56544.2022.10037509},
isbn = {9781665463874},
year  = {2022},
date = {2022-01-01},
journal = {Proceedings - International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2022},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The COVID-19 pandemic has become a serious problem that has attacked various aspects of life such as social, economic, religious, and others. The government has held a COVID-19 vaccination program as an effort to deal with the COVID-19 problem since January 13, 2021. Many problems occurred due to difficulties in dividing the vaccination recipient areas. This is due to the large number of regions with different conditions for each region. One of the efforts to assist the process of processing large vaccination data is data mining techniques and using the clustering method with the K-medoids algorithm. In this study, data on COVID-19 vaccination was grouped in the East Jakarta area using the K-medoids algorithm clustering method. The calculation is carried out using the Euclidean Distance equation and the value of S > 0. The grouped area categories are at the kelurahan level which will then be divided into several clusters. The clustering process was carried out with RapidMiner on 267 kelurahan data on four main attributes, namely the number of targets, the number of vaccine doses 1, the number of vaccine doses 2, and the number of vaccine doses 3. The clustering process was carried out in 6 simulations with variations of k medoids as much as 2 to 7. The results of clustering show the best number of clusters obtained in the simulation is cluster 6 with the smallest Davies Bouldin Index (DBI) value of 0.209. The clusters obtained are clusters 0 to cluster 5. The cluster that is prioritized in giving vaccinations is cluster 2 with 67 items because its members are areas in DKI Jakarta and give a high score in cases of COVID-19 compared to other clusters. © 2022 IEEE.},
note = {cited By 1; Conference of 4th International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2022 ; Conference Date: 23 November 2022 Through 24 November 2022; Conference Code:186654},
keywords = {Cluster analysis; Clustering algorithms; Data mining; Vaccines, Clustering methods; Clustering process; Clusterings; Condition; Jakarta; K-medoids; K-medoids algorithms; Pandemic; Social-economic; Vaccination, COVID-19},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miranda2022103,
title = {Crowdsensing on Smart Cities: A Systematic Review},
author = {R. Miranda and V. Ramos and E. Ribeiro and C. Rodrigues and A. Silva and D. Durães and C. Analide and A. Abelha and J. Machado},
editor = {Rodriguez Ribon J. C. Ferro M. Bicharra Garcia A.C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148698891&doi=10.1007%2f978-3-031-22419-5_9&partnerID=40&md5=400c3090fe6a5ffac109f06f5f487953},
doi = {10.1007/978-3-031-22419-5_9},
issn = {03029743},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13788 LNAI},
pages = {103-106},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {With the rise of the internet and the Internet of Things (IoT), the concept of a Smart City began to materialise. Crowdsensing is the process of using portable sensing devices to gather information about people’s surroundings. Furthermore, the research on these domains has pivoted from solely technology concepts to now including how they improve the quality of life of citizens and their utility. This paper presents a systematic review aiming to identify the role and purpose of crowdsensing and the improvement of citizens’ lives in a smart city through this technique. Using the SCOPUS citation and abstract database, six papers were picked out as relevant for discussion in the review. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 2; Conference of 17th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2022 ; Conference Date: 23 November 2022 Through 25 November 2022; Conference Code:289599},
keywords = {Crowdsensing; Quality of life; Sensing devices; Smart notification; Systematic Review, Internet of things, Smart city},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Montenegro2022231,
title = {Evaluation of Transfer Learning to Improve Arrhythmia Classification for a Small ECG Database},
author = {L. Montenegro and H. Peixoto and J. M. Machado},
editor = {Rodriguez Ribon J. C. Ferro M. Bicharra Garcia A.C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148688129&doi=10.1007%2f978-3-031-22419-5_20&partnerID=40&md5=93bdc43f3a02b8d7307904e856cbc2f5},
doi = {10.1007/978-3-031-22419-5_20},
issn = {03029743},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13788 LNAI},
pages = {231-242},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Deep learning algorithms automatically extract features from ECG signals, eliminating the manual feature extraction step. Deep learning approaches require extensive data to be trained, and access to an ECG database with a large variety of cardiac rhythms is limited. Transfer learning is a possible solution to improve the results of cardiac rhythms classification in a small database. This work proposes a open-access robust 1D-CNN model to be trained with a public database containing cardiac rhythms with their annotations. This study explores transfer learning in a small database to improve arrhythmia classification tasks. Overall, the 1D-CNN model trained without TL achieved an average accuracy of 91.73 % and F1-score 67.18 %; meanwhile, the 1D-CNN model with TL achieved an average accuracy of 94.40 % and F1-score of 79.72 %. The F1-score has an overall improvement of 12.54 % over the baseline model for rhythm classification. Moreover, this method significantly improved the F1-score precision and recall, making the model trained with transfer learning more relevant and reliable. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 1; Conference of 17th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2022 ; Conference Date: 23 November 2022 Through 25 November 2022; Conference Code:289599},
keywords = {Arrhythmia classification; Cardiac rhythms; CNN models; Deep learning; ECG classification; ECG signals; F1 scores; Features extraction; Heart rhythm; Transfer learning, Classification (of information); Database systems; Deep learning; Diseases; Heart; Learning algorithms, Electrocardiograms},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva2022202,
title = {Traffic Light Optimization of an Intersection: A Portuguese Case Study},
author = {G. O. Silva and A. M. A. C. Rocha and G. R. Witeck and A. Silva and D. Durães and J. Machado},
editor = {Fernandes F. P. Kosir A. Pereira A.I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148012915&doi=10.1007%2f978-3-031-23236-7_15&partnerID=40&md5=79c08f2493a1ee2d54f8046f4bcbb979},
doi = {10.1007/978-3-031-23236-7_15},
issn = {18650929},
year  = {2022},
date = {2022-01-01},
journal = {Communications in Computer and Information Science},
volume = {1754 CCIS},
pages = {202-214},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Smart cities aim to rise strategies that reduce issues caused by the urban population growth and fast urbanization. Thus, traffic light optimization emerges as an important option for urban traffic management. The main goal of this study is to improve traffic light management at a specific intersection, in the City of Guimarães (Portugal), where high-intensity traffic and an active pedestrian area were observed, generating traffic queues. To achieve the goals, a simulation-based optimization strategy using the Particle Swarm Optimization combined with the Simulation of Urban Mobility software was used to minimize the average waiting time of the vehicles by determining the optimal value of the traffic light cycle. The computational results showed it is possible to decrease by 78.2% the average value of the waiting time. In conclusion, by better managing the traffic light cycle time, traffic flow without congestion or queues can be achieved. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 1; Conference of 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022 ; Conference Date: 24 October 2022 Through 25 October 2022; Conference Code:289139},
keywords = {Case-studies; Light cycles; Optimisations; Particle swarm; Particle swarm optimization; Simulation of urban mobility; Swarm optimization; Traffic light; Urban mobility; Urban population, Computer software, Particle swarm optimization (PSO); Population statistics; Street traffic control; Traffic congestion; Traffic signals; Urban growth},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sousa2022199,
title = {The Covid-19 Influence on the Desire to Stay at Home: A Big Data Architecture},
author = {R. Sousa and D. Oliveira and A. Carneiro and L. Pinto and A. Pereira and A. Peixoto and H. Peixoto and J. Machado},
editor = {Tino P. Camacho D. Yin H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144825704&doi=10.1007%2f978-3-031-21753-1_20&partnerID=40&md5=07933409c2e62457b481165cbb7438e0},
doi = {10.1007/978-3-031-21753-1_20},
issn = {03029743},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13756 LNCS},
pages = {199-210},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The COVID-19 pandemic has had an impact on many aspects of society in recent years. The ever-increasing number of daily cases and deaths makes people apprehensive about leaving their homes without a mask or going to crowded places for fear of becoming infected, especially when vaccination was not available. People were expected to respect confinement rules and have their public events cancelled as more restrictions were imposed. As a result of the pandemic’s insecurity and instability, people became more at ease at home, increasing their desire to stay at home. The present research focuses on studying the impact of the COVID-19 pandemic on the desire to stay at home and which metrics have a greater influence on this topic, using Big Data tools. It was possible to understand how the number of new cases and deaths influenced the desire to stay at home, as well as how the increase in vaccinations influenced it. Moreover, investigated how gatherings and confinement restrictions affected people’s desire to stay at home. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022 ; Conference Date: 24 November 2022 Through 26 November 2022; Conference Code:287419},
keywords = {Behavior analysis; Business-intelligence; Data architectures; Data tools; New case; Research focus; Stay at home, Big data, COVID-19},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2022265,
title = {OpenEHR modelling applied to Complementary Diagnostics Requests},
author = {D. Oliveira and A. Santos and D. Braga and I. Silva and R. Sousa and A. Abelha and J. Machado},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144824043&doi=10.1016%2fj.procs.2022.10.148&partnerID=40&md5=860a35222661de3243855a8f9f6a295a},
doi = {10.1016/j.procs.2022.10.148},
issn = {18770509},
year  = {2022},
date = {2022-01-01},
journal = {Procedia Computer Science},
volume = {210},
number = {C},
pages = {265-270},
publisher = {Elsevier B.V.},
abstract = {Complementary Diagnostic Requests (CDRs) are required for disease identification, monitoring, and prognosis. Diagnostic tests misuse, on the other hand, can lead to negative health outcomes as well as additional costs. Inappropriate diagnostic test requests are primarily the result of a lack of interoperability between Healthcare Information Systems (HIS). On one hand, clinicians can be mislead into which test is the best option for each clinical case, on the other hand missing previous results, leads to duplication or unnecessary tests. HIS is increasingly relying on standards based on dual architecture to promote interoperability as well as the structuring and consistency of clinical and demographic data. The OpenEHR standard's duo-based architecture allows for concise modelling of archetypes and templates for a given clinical case, which was used in this study. As a result, the purpose of this research was to build an openEHR template for the CDR registration as well as the architecture of a Data Warehouse (DW) system capable of storing all of the information needed for the diagnostic test request process. Afterwards, Business Intelligence (BI) indicators was developed in order to answers the needs for test registration and execution. © 2022 Elsevier B.V.. All rights reserved.},
note = {cited By 2; Conference of 13th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN / The 12th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2022 / Affiliated Workshops ; Conference Date: 26 October 2022 Through 28 October 2022; Conference Code:148658},
keywords = {Additional costs; Archetype; Business-intelligence; Clinical data; Complementary diagnostic request; Demographic data; Diagnostic tests; Health outcomes; OpenEHR; Template, Architecture; Data warehouses; Diagnosis; Information analysis, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sousa2022315,
title = {Medical Recommendation System Based on Daily Clinical Reports: A Proposed NLP Approach for Emergency Departments},
author = {R. Sousa and D. Oliveira and D. Durães and C. Neto and J. Machado},
editor = {Stahl F. Bramer M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144818897&doi=10.1007%2f978-3-031-21441-7_24&partnerID=40&md5=c46b6c78b09ded01e7ecd348b5a7dea6},
doi = {10.1007/978-3-031-21441-7_24},
issn = {03029743},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13652 LNAI},
pages = {315-320},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The operational management of an emergency department (ED) requires more attention from hospital administration since it can have a global impact on the institution’s management, increasing the probability of adverse events and worsening hospital expenses. Effective management of an ED potentially results in fewer hospitalisations after an ED admission. The purpose of the present study is to perform a multi-class prediction based on: a) structured data and unstructured data in an ED episode; and b) unstructured data generated during the inpatient event, just after the ED episode. The designed prediction model will lay the foundation for an ED Decision Support System based on symptoms and principal diagnoses. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022 ; Conference Date: 13 December 2022 Through 15 December 2022; Conference Code:287589},
keywords = {Artificial intelligence; Diagnosis; Emergency rooms; Natural language processing systems; Recommender systems, Decision support systems, Emergency departments; Emergency health department; Global impacts; Hospital administration; Language processing; Natural language processing; Natural languages; Operational management; Text-mining; Unstructured data},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Afonso2022271,
title = {OpenEHR based bariatric surgery follow-up},
author = {A. Afonso and C. Alvarez and D. Ferreira and D. Oliveira and H. Peixoto and A. Abelha and J. Machado},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144818896&doi=10.1016%2fj.procs.2022.10.149&partnerID=40&md5=948521a51fd931ae8163d47a7fd3e7a0},
doi = {10.1016/j.procs.2022.10.149},
issn = {18770509},
year  = {2022},
date = {2022-01-01},
journal = {Procedia Computer Science},
volume = {210},
number = {C},
pages = {271-276},
publisher = {Elsevier B.V.},
abstract = {According to the World Health Organization (WHO), more than one billion people in the world are obese, and this number is still increasing. When this disease becomes a high risk to the individual's health and the non-invasive approach does not result in weight loss, it is usual to resort to an invasive intervention, bariatric surgery. Due to all the specifications, as well as the multidisciplinary treatment inherent to this procedure, the need for a specialized environment emerges. In this context, the main objective of this topic focuses on the development of a platform for registration and monitoring of bariatric surgery. For this purpose, a web platform was created, which integrates openEHR forms for the registration of appointments regarding this intervention, and uses openEHR modeling to build the interoperable template. The development of this project aims to provide greater ease and speed in patient care, assisting health professionals in their daily lives. © 2022 Elsevier B.V.. All rights reserved.},
note = {cited By 2; Conference of 13th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN / The 12th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2022 / Affiliated Workshops ; Conference Date: 26 October 2022 Through 28 October 2022; Conference Code:148658},
keywords = {Bariatric surgery; Electronic health; Electronic health record; Follow up; Health information systems; Health records; Obesity; OpenEHR; Weight loss; World Health Organization, Health risks; Surgery, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sousa202272,
title = {Sustainable and Social Energy on Smart Cities: Systematic Review},
author = {R. Sousa and D. Lopes and A. Silva and D. Durães and H. Peixoto and J. Machado and P. Novais},
editor = {Augusto M. F. Portela F. Guarda T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144627809&doi=10.1007%2f978-3-031-20316-9_6&partnerID=40&md5=e346e8afe9e3ac6ba198bcf05c775a97},
doi = {10.1007/978-3-031-20316-9_6},
issn = {18650929},
year  = {2022},
date = {2022-01-01},
journal = {Communications in Computer and Information Science},
volume = {1676 CCIS},
pages = {72-84},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Sustainability and social energy are two concepts associated with smart cities. They aim to combat and contain the alarming environmental and socio-economic repercussions that urbanization has been causing on our planet. Smart sustainable cities drive to improve the life quality of citizens while ensuring that they meet the needs of the current and future generations. Sustainability is essential for urban transformation to achieve more resource-efficient, resilient and smart cities. The main objective of sustainable cities is to guide decisions for interventions in the city. Monitoring systems are examples of measures that aspire to ensure greater sustainability and energy efficiency, such as the application of air quality meters or smart water and light meters. Throughout the analysis of the collected data, it’s possible to develop alert systems and optimization models considering various metrics based on artificial intelligence. Therefore, allowing users to make better decisions to positively affect the course of actions in their cities and make it possible to apply sustainability and social energy measures. Thus, it is possible to reduce and improve the consumption of natural resources. Industry 5.0 is crucial in the evolution of smart cities. The complementarity role that this industry has been demonstrating is related to the technologies being developed, in which artificial intelligence plays an important role. This industry places its technology at the service of human beings, society and the environment. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 1; Conference of 2nd International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2022 ; Conference Date: 12 September 2022 Through 15 September 2022; Conference Code:286829},
keywords = {Air quality; Artificial intelligence; Digital storage; Smart city; Sustainable development, Current generation; Energy; Environmental economics; Life qualities; Monitoring system; Smart sustainable city; Social energy; Socio-economics; Sustainable cities; Systematic Review, Energy efficiency},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2022222,
title = {Improving the Effectiveness of Heart Disease Diagnosis with Machine Learning},
author = {C. Oliveira and R. Sousa and H. Peixoto and J. Machado},
editor = {Fernandez A. Almeida A. Gonzalez-Briones A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141686201&doi=10.1007%2f978-3-031-18697-4_18&partnerID=40&md5=5b5ddaa362353509a172400b23b54b71},
doi = {10.1007/978-3-031-18697-4_18},
issn = {18650929},
year  = {2022},
date = {2022-01-01},
journal = {Communications in Computer and Information Science},
volume = {1678 CCIS},
pages = {222-231},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Despite technological and clinical improvements, heart disease remains one of the leading causes of death worldwide. A significant shift in the paradigm would be for medical teams to be able to accurately identify, at an early stage, whether a patient is at risk of developing or having heart disease, using data from their health records paired with Data Mining tools. As a result, the goal of this research is to determine whether a patient has a cardiac condition by using Data Mining methods and patient information to aid in the construction of a Clinical Decision Support System. With this purpose, we use the CRISP-DM technique to try to forecast the occurrence of cardiac disorders. The greatest results were obtained utilizing the Random Forest technique and the Percentage Split sampling method with a 66% training rate. Other approaches, such as Naïve Bayes, J48, and Sequential Minimal Optimization, also produced excellent results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 1; Conference of 20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022 ; Conference Date: 13 July 2022 Through 15 July 2022; Conference Code:285119},
keywords = {Cardiology; Classification (of information); Clinical research; Data mining; Decision trees; Diagnosis; Diseases; Health risks; Heart; Machine learning; Optimization, Causes of death; Condition; Data mining methods; Data-mining tools; Health records; Heart disease; Heart disease diagnosis; Machine-learning; Medical teams; Patient information, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Alves2022393,
title = {Survey for Big Data Platforms and Resources Management for Smart Cities},
author = {C. Alves and A. Chaves and C. Rodrigues and E. Ribeiro and A. Silva and D. Durães and J. Machado and P. Novais},
editor = {Martinez de Pison F. J. Perez Garcia H. Garcia Bringas P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139072493&doi=10.1007%2f978-3-031-15471-3_34&partnerID=40&md5=e9ed3982c3388f81adf3383436500587},
doi = {10.1007/978-3-031-15471-3_34},
issn = {03029743},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13469 LNAI},
pages = {393-404},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Currently, smart cities are a hot topic and their tendency will be to optimize resources and promote efficient strategies for the preservation of the planet as well as to increase the quality of life of its inhabitants. In this sense, this research presents an initial component of investigation about Big Data Platforms for Smart Cities in order to be implemented in integrated and innovative solutions for development in urban centers. For this, a survey was carried out on “Big Data Platforms”, “Data Science Platforms”, “Security & Privacy” and “Resources Management”. The extraction of the results of this research was done through the SCOPUS repository in articles from the last 5 years to conclude what has been done so far and what will be the trends in the coming years, define proposals for possible solutions for smart cities and identify the right technologies for the design of a smart city architecture. © 2022, Springer Nature Switzerland AG.},
note = {cited By 1; Conference of 17th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2022 ; Conference Date: 5 September 2022 Through 7 September 2022; Conference Code:283099},
keywords = {Big data, Big data platform; Data platform; Data resources; Efficient strategy; Hot topics; Platform management; Privacy; Quality of life; Resource management; Security, Data privacy; Data Science; Information management; Internet of things; Smart city; Surveys},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira2022285,
title = {Comparison of Different Deployment Approaches of FPGA-Based Hardware Accelerator for 3D Object Detection Models},
author = {P. Pereira and A. Linhares Silva and R. Machado and J. Silva and D. Durães and J. Machado and P. Novais and J. Monteiro and P. Melo-Pinto and D. Fernandes},
editor = {Paiva A. Martins B. Marreiros G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138741507&doi=10.1007%2f978-3-031-16474-3_24&partnerID=40&md5=cee8ca98871acfc2bf58e53a13e9eefa},
doi = {10.1007/978-3-031-16474-3_24},
issn = {03029743},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13566 LNAI},
pages = {285-296},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {GPU servers have been responsible for the recent improvements in the accuracy and inference speed of the object detection models targeted to autonomous driving. However, its features, namely, power consumption and dimension, make its integration in autonomous vehicles impractical. Hybrid FPGA-CPU boards emerged as an alternative to server GPUs in the role of edge devices in autonomous vehicles. Despite their energy efficiency, such devices do not offer the same computational power as GPU servers and have fewer resources available. This paper investigates how to deploy deep learning models tailored to object detection in point clouds in edge devices for onboard real-time inference. Different approaches, requiring different levels of expertise in logic programming applied to FPGAs, are explored, resulting in three main solutions: utilization of software tools for model adaptation and compilation for a proprietary hardware IP; design and implementation of a hardware IP optimized for computing traditional convolutions operations; design and implementation of a hardware IP optimized for sparse convolutions operations. The performance of these solutions is compared in the KITTI dataset with computer performances. All the solutions resort to parallelism, quantization and optimized access control to memory to reduce the usage of logical FPGA resources, and improve processing time without significantly sacrificing accuracy. Solutions probed to be effective for real-time inference, power limited and space-constrained purposes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 2; Conference of 21st EPIA Conference on Artificial Intelligence, EPIA 2022 ; Conference Date: 31 August 2022 Through 2 September 2022; Conference Code:283109},
keywords = {Autonomous vehicles; Computation theory; Computational efficiency; Convolution; Deep learning; Energy efficiency; Field programmable gate arrays (FPGA); Graphics processing unit; Integrated circuit design; Object recognition; Optical radar; Program processors, Autonomous Vehicles; Design and implementations; Detection models; FPGA-based hardware accelerators; Hardware accelerators; Hardware IP; Light detection and ranging; Objects detection; Real-time inference, Object detection},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira2022551,
title = {A Comparison of Automated Time Series Forecasting Tools for Smart Cities},
author = {P. J. Pereira and N. Costa and M. Barros and P. Cortez and D. Durães and A. Silva and J. Machado},
editor = {Paiva A. Martins B. Marreiros G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138674457&doi=10.1007%2f978-3-031-16474-3_45&partnerID=40&md5=cc3bc2f19869e8c4a50d3a836654d054},
doi = {10.1007/978-3-031-16474-3_45},
issn = {03029743},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13566 LNAI},
pages = {551-562},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Most smart city sensors generate time series records and forecasting such data can provide valuable insights for citizens and city managers. Within this context, the adoption of Automated Time Series Forecasting (AutoTSF) tools is a key issue, since it facilitates the design and deployment of multiple TSF models. In this work, we adapt and compare eight recent AutoTSF tools (Pmdarima, Prophet, Ludwig, DeepAR, TFT, FEDOT, AutoTs and Sktime) using nine freely available time series that can be related with the smart city concept (e.g., temperature, energy consumption, city traffic). An extensive experimentation was carried out by using a realistic rolling window with several training and testing iterations. Also, the AutoTSF tools were evaluated by considering both the predictive performances and required computational effort. Overall, the FEDOT tool presented the best overall performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 2; Conference of 21st EPIA Conference on Artificial Intelligence, EPIA 2022 ; Conference Date: 31 August 2022 Through 2 September 2022; Conference Code:283109},
keywords = {Automated machine learning; Automated machines; City traffic; Energy-consumption; Forecasting tools; Key Issues; Machine-learning; Rolling window; Time series forecasting; Times series, Automation; Energy utilization; Forecasting; Smart city; Time series, Machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva202268,
title = {On Tuning the Particle Swarm Optimization for Solving the Traffic Light Problem},
author = {G. O. Silva and A. M. A. C. Rocha and G. R. Witeck and A. Silva and D. Durães and J. Machado},
editor = {Misra S. Murgante B. Gervasi O.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135961179&doi=10.1007%2f978-3-031-10562-3_6&partnerID=40&md5=b2d6908c90ece4348548f01963ab55a9},
doi = {10.1007/978-3-031-10562-3_6},
issn = {03029743},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13378 LNCS},
pages = {68-80},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In everyday routines, there are multiple situations of high traffic congestion, especially in large cities. Traffic light timed regulated intersections are one of the solutions used to improve traffic flow without the need for large-scale and costly infrastructure changes. A specific situation where traffic lights are used is on single-lane roads, often found on roads under maintenance, narrow roads or bridges where it is impossible to have two lanes. In this paper, a simulation-optimization strategy is tested for this scenario. A Particle Swarm Optimization algorithm is used to find the optimal solution to the traffic light timing problem in order to reduce the waiting times for crossing the lane in a simulated vehicle system. To assess vehicle waiting times, a network is implemented using the Simulation of Urban MObility software. The performance of the PSO is analyzed by testing different parameters of the algorithm in solving the optimization problem. The results of the traffic light time optimization show that the proposed methodology is able to obtain a decrease of almost 26% in the average waiting times. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 2; Conference of 22nd International Conference on Computational Science and Its Applications , ICCSA 2022 ; Conference Date: 4 July 2022 Through 7 July 2022; Conference Code:281299},
keywords = {Bridges; Computer software; Particle swarm optimization (PSO); Street traffic control; Traffic signals, Large cities; Particle swarm; Particle swarm optimization; Simulation of urban mobility; Swarm optimization; Traffic flow; Traffic light; Traffic light problem; Urban mobility; Waiting time, Traffic congestion},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Witeck202243,
title = {A Bibliometric Review and Analysis of Traffic Lights Optimization},
author = {G. R. Witeck and A. M. A. C. Rocha and G. O. Silva and A. Silva and D. Durães and J. Machado},
editor = {Misra S. Murgante B. Gervasi O.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135878912&doi=10.1007%2f978-3-031-10562-3_4&partnerID=40&md5=cb31d8b72c37fbf91545fbf66609cc1e},
doi = {10.1007/978-3-031-10562-3_4},
issn = {03029743},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13378 LNCS},
pages = {43-54},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The significant increase in the number of vehicles in urban areas emerges the challenge of urban mobility. Researchers in this area suggest that most daily delays in urban travel times are caused by intersections, which could be reduced if the traffic lights at these intersections were more efficient. The use of simulation for real intersections can be effective in optimizing the cycle times and improving the traffic light timing to coordinate vehicles passing through intersections. From these themes emerge the research questions: How are the existing approaches (optimization techniques and simulation) to managing traffic lights smartly? What kind of data (offline and online) are used for traffic lights optimization? How beneficial is it to propose an optimization approach to the traffic system? This paper aims to answer these questions, carried out through a bibliometric literature review. In total, 93 articles were analyzed. The main findings revealed that the United States and China are the countries with the most studies published in the last ten years. Moreover, Particle Swarm Optimization is a frequently used approach, and there is a tendency for studies to perform optimization of real cases by real-time data, showing that the praxis of smart cities has resorted to smart traffic lights. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 22nd International Conference on Computational Science and Its Applications , ICCSA 2022 ; Conference Date: 4 July 2022 Through 7 July 2022; Conference Code:281299},
keywords = {Bibliometric; Cycle time; Number of vehicles; Optimisations; Research questions; Traffic light; Travel-time; Urban areas; Urban mobility; Urban travels, Particle swarm optimization (PSO); Travel time, Smart city},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Burduk2022,
title = {Efficiency forecasting of electric bundle assembly with use of ANN model},
author = {A. Burduk and B. Dybała and J. MacHado},
editor = {Xavior M. A. Burduk A. Batako A.D.L.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134739780&doi=10.1088%2f1742-6596%2f2198%2f1%2f012001&partnerID=40&md5=76ecb3dac2c40e4ab964e68e0041091a},
doi = {10.1088/1742-6596/2198/1/012001},
issn = {17426588},
year  = {2022},
date = {2022-01-01},
journal = {Journal of Physics: Conference Series},
volume = {2198},
number = {1},
publisher = {Institute of Physics},
abstract = {Management of production systems requires making immediate decisions based on the data generated in bulk by IT systems. In this case, it can be helpful to use models of artificial neural networks (ANN) that, on the grounds of accessible data, will determine results of the made decision. One of the key problems in production companies is determination of execution time and cost of a production order. The problem is especially important in a company manufacturing a variable product line with a big part of manual operations. In the article, the way of building an ANN model for efficiency forecasting of the assembly process of electric bundles is presented. With regard to the very wide and variable product line, the products with different complexity degree are manufactured on three types of assembly lines. The assembly processes are performed on the assembly lines manually by groups of workers, so efficiency of the process is influenced mostly by skills and experience of these workers. Therefore, numbers of new assembly workers assigned to individual assembly lines and quantities of new products in the production schedule are selected as explanatory variables in the ANN model. The explained variable in the ANN model is production volume of the manufactured electric bundles. © Published under licence by IOP Publishing Ltd.},
note = {cited By 0; Conference of 15th Global Congress on Manufacturing and Management, GCMM 2021 ; Conference Date: 25 November 2020 Through 27 November 2020; Conference Code:180965},
keywords = {Artificial neural network modeling; Assembly line; Assembly process; Decision-based; IT system; Production companies; Production system; Productline; Use-model; Workers', Assembly, Assembly machines; Information management; Manufacture; Neural networks; Production control; Production efficiency},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva2022726,
title = {Rule-based Clinical Decision Support System using the OpenEHR Standard},
author = {S. T. Silva and F. Hak and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132194511&doi=10.1016%2fj.procs.2022.03.098&partnerID=40&md5=c8c752f5ce49f27dbc82c7ed9abc4291},
doi = {10.1016/j.procs.2022.03.098},
issn = {18770509},
year  = {2022},
date = {2022-01-01},
journal = {Procedia Computer Science},
volume = {201},
number = {C},
pages = {726-731},
publisher = {Elsevier B.V.},
abstract = {Clinical Decision Support Systems present diverse ramifications that ultimately help healthcare professionals in their decision-making process. These systems can manifest themselves in the form of computerized guidelines that, depending on their goal and stipulated directives, help and optimize healthcare professional's daily tasks. However, nowadays there is a certain resistance from the healthcare community towards using these systems, with valid justifications such as: the absence of transparency in defined rules, the lack of interoperability within these systems and the difficulty in their usage as they prove themselves unintuitive and hard. With the intention of culminating these flaws, a clinical decision support system was developed based on the openEHR module in order to ensure standardization and semantic interoperability. This system will be oriented towards management and creation of clinical guidelines based on the specifications of the openEHR standard. © 2022 Elsevier B.V.. All rights reserved.},
note = {cited By 1; Conference of 13th International Conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022 ; Conference Date: 22 March 2022 Through 25 March 2022; Conference Code:147558},
keywords = {Artificial intelligence; Decision making; Decision support systems; Health care; Semantics, Clinical decision support systems; Daily tasks; Decision module language; Decision modules; Decision-making process; Guideline; Health care professionals; Openehr; Rule; Rule based, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marques2022720,
title = {Pervasive Monitoring System for Services and Servers in Healthcare Environment},
author = {C. Marques and V. Ramos and H. Peixoto and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132186057&doi=10.1016%2fj.procs.2022.03.097&partnerID=40&md5=e46c2b47a98b1e8446e72ab0b00c1ea2},
doi = {10.1016/j.procs.2022.03.097},
issn = {18770509},
year  = {2022},
date = {2022-01-01},
journal = {Procedia Computer Science},
volume = {201},
number = {C},
pages = {720-725},
publisher = {Elsevier B.V.},
abstract = {Information systems are continuously evolving in nature and complexity. In the healthcare environment, information and information exchange are critical for providing health care at all levels. Hence, the healthcare environment is particularly relevant when discussing IT infrastructure monitoring and disaster prevention since availability and communication are vital for the proper functioning of healthcare units, whether acting in isolation or on a network. This work focuses on understanding what comprises a good monitoring solution, analyzing the monitoring solutions currently available in the optics of a multi-location healthcare environment and, finally, proposing a pervasive and comprehensive conceptual architecture for a monitoring system that is capable of handling such environments. © 2022 Elsevier B.V.. All rights reserved.},
note = {cited By 3; Conference of 13th International Conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022 ; Conference Date: 22 March 2022 Through 25 March 2022; Conference Code:147558},
keywords = {Disaster prevention, Environment information; Health information systems; Healthcare environments; Information exchanges; Infrastructure monitoring; IT infrastructures; IT monitoring system; Microservice; Monitoring system; Pervasive monitoring, Health care; Information systems; Information use; Monitoring},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marques202263,
title = {Predicting Diabetes Disease in the Female Adult Population, Using Data Mining},
author = {C. Marques and V. Ramos and H. Peixoto and J. Machado},
editor = {Goleva R. Silva B. Spinsante S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127917985&doi=10.1007%2f978-3-030-99197-5_6&partnerID=40&md5=d0b1061ffb8b1c4ae3d164d82c9a4a30},
doi = {10.1007/978-3-030-99197-5_6},
issn = {18678211},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {432 LNICST},
pages = {63-73},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The aim of this study is to predict, through data mining, the incidence of diabetes disease in the Pima Female Adult Population. Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces and is a major cause of blindness, kidney failure, heart attacks, stroke and lower limb amputation. The information collected from this population combined with the data mining techniques, may help to detect earlier the presence of this decease. To achieve the best possible ML model, this work uses the CRISP-DM methodology and compares the results of five ML models (Logistic Regression, Naive Bayes, Random Forest, Gradient Boosted Trees and k-NN) obtained from two different datasets (originated from two different data preparation strategies). The study shows that the most promising model as k-NN, which produced results of 90% of accuracy and also 90% of F1 Score, in the most realistic evaluation scenario. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
note = {cited By 0; Conference of 8th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2021 ; Conference Date: 24 November 2021 Through 26 November 2021; Conference Code:275359},
keywords = {Adult populations; Chronic disease; CRISP-DM; Data-mining techniques; Female adults; Heart attack; Logistics regressions; Lower-limb amputations; ML model; Naive bayes, Barium compounds; Decision trees; Insulin; Logistic regression; Nearest neighbor search; Population statistics, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Duarte202253,
title = {A Comparative Study of Data Mining Techniques Applied to Renal-Cell Carcinomas},
author = {A. Duarte and H. Peixoto and J. Machado},
editor = {Goleva R. Silva B. Spinsante S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127847164&doi=10.1007%2f978-3-030-99197-5_5&partnerID=40&md5=df7b9e6c1d403d0e3049ede31cd77307},
doi = {10.1007/978-3-030-99197-5_5},
issn = {18678211},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {432 LNICST},
pages = {53-62},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Despite being one of the deadliest diseases and the enormous evolution in fighting it, the best methods to predict kidney cancer, namely Renal-Cell Carcinomas (RCC), are not well-known. One of the solutions to accelerate the current knowledge about RCC is through the use of Data Mining techniques based on patients' personal and clinical data. Therefore, it is crucial to understand which techniques are the most suitable to extract knowledge about this disease. In this paper, we followed the CRISP-DM methodology to simulate different techniques to determine the ones with the best predictive performance. For this purpose, we used a dataset of 821 records of RCC patients, obtained from The Cancer Genome Atlas. The present work tests different Data Mining techniques, that can be used to predict the 5-year life expectancy of patients with renal cancer and to predict the number of days to death for patients who have a life expectancy of less than 5 years. The results obtained demonstrated that the best algorithm for estimating the vital status at 5 years was Random Forest. This algorithm presented an accuracy of 87.65% and an AUROC of 0.931. For the prediction of days to death, the best performance was obtained with the k-Nearest Neighbors algorithm with a root mean square error of 354.6 days. The work suggested that Data Mining techniques can help to understand the influence of various risk factors on the life expectancy of patients with RCC. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
note = {cited By 1; Conference of 8th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2021 ; Conference Date: 24 November 2021 Through 26 November 2021; Conference Code:275359},
keywords = {'current; Clinical data; Comparatives studies; CRISP-DM; Data-mining techniques; Kidney cancer; Life expectancies; Rapidminer; Renal cell carcinoma; Survival, Data mining, Decision trees; Diseases; Forecasting; Mean square error; Nearest neighbor search},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Guisasola2022271,
title = {Detecting Autism Spectrum Disorder Using Data Mining},
author = {A. C. Guisasola and D. Ferreira and C. Neto and A. Abelha and J. Machado},
editor = {Riola Rodriguez J. M. Fajardo-Toro C.H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119376845&doi=10.1007%2f978-981-16-4884-7_22&partnerID=40&md5=0b7fd71f51e6e6841168346ba356b94e},
doi = {10.1007/978-981-16-4884-7_22},
issn = {21903018},
year  = {2022},
date = {2022-01-01},
journal = {Smart Innovation, Systems and Technologies},
volume = {255},
pages = {271-281},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Autism spectrum disorder (ASD) is a set of neurodevelopmental disorders that affect cognitive development, social and communication skills, and behavior of affected individuals. The faster traces of ASD are identified, the faster the stimulation will begin and the more effective the gains in neuropsychomotor development will be. That being said, the earlier the diagnosis of ASD, the easier it is to control the disorder. Therefore, this study aims to classify the cases of ASD as “yes” if a patient has been diagnosed with ASD or “no” if a patient has not, using data mining (DM) models with classification techniques. The methodology of cross-industry standard process for data mining (CRISP-DM) was followed, and to induce the data mining models, the Rapidminer software was used. The results were quite promising reaching a level of accuracy of 97%, specificity of 95.45%, sensitivity of 100%, and precision of 95.65%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
note = {cited By 0; Conference of Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2021 ; Conference Date: 18 August 2021 Through 20 August 2021; Conference Code:267889},
keywords = {Autism spectrum disorders; Cognitive communications; Cognitive development; Communication skills; Cross industry; Cross-industry standard process for data mining; Data mining models; Industry standards; Social skills; Standards process, Classification (of information); Diseases, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neto2022283,
title = {Classification of Dementia in Adults},
author = {C. Neto and D. Ferreira and J. Nunes and L. Braga and L. Martins and L. Cunha and J. Machado},
editor = {Riola Rodriguez J. M. Fajardo-Toro C.H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119352719&doi=10.1007%2f978-981-16-4884-7_23&partnerID=40&md5=c3b7bd8b729c23c8a694e7e6807db3cd},
doi = {10.1007/978-981-16-4884-7_23},
issn = {21903018},
year  = {2022},
date = {2022-01-01},
journal = {Smart Innovation, Systems and Technologies},
volume = {255},
pages = {283-293},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Dementia is a broad term for a large number of conditions, and it is often associated with Alzheimer’s disease. A reliable diagnosis of this disease, especially in the early stages, may prevent further complications. As such, machine learning algorithms can be applied in order to validate and correctly classify cases of dementia or non dementia in adults, assisting physicians in the diagnosis and management of this clinical condition. In this study, a dataset containing magnetic resonance imaging comparisons of demented/non demented adults was used to conduct a Data Mining process, following the Cross Industry Standard Process for Data Mining methodology, with the main goal of classifying instances of dementia. Different machine learning algorithms were applied during this process, more specifically Support Vector Machines, Decision Trees, Logistic Regression, Neural Networks, Naïve Bayes and Random Forest. The maximum accuracy of 95.41% was achieved with the Naïve Bayes algorithm using Split Validation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
note = {cited By 0; Conference of Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2021 ; Conference Date: 18 August 2021 Through 20 August 2021; Conference Code:267889},
keywords = {Alzheimer; Clinical conditions; Condition; Cross industry; Data mining process; Industry standards; Machine decisions; Machine learning algorithms; Standards process; Support vectors machine, Classification (of information), Data mining; Decision trees; Diagnosis; Learning algorithms; Magnetic resonance imaging; Neurodegenerative diseases; Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}
@book{González2022v,
title = {Preface},
author = {S. R. González and J. M. Machado and A. González-Briones and J. Wikarek and R. Loukanova and G. Katranas and R. Casado-Vara},
editor = {Gonzalez-Briones A. Machado J.M. Gonzalez S.R.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115432630&doi=10.1016%2fB978-0-12-809464-8.05001-6&partnerID=40&md5=c2ada6458b97c096a01f3de9bede5911},
doi = {10.1016/B978-0-12-809464-8.05001-6},
issn = {23673370},
year  = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {332},
pages = {v-vi},
publisher = {Springer Science and Business Media Deutschland GmbH},
note = {cited By 0; Conference of 18th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2021 ; Conference Date: 6 October 2021 Through 8 October 2021; Conference Code:264809},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
@article{Lori2021,
title = {Quantum field theory representation in quantum computation},
author = {N. Lori and J. Neves and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120169172&doi=10.3390%2fapp112311272&partnerID=40&md5=020ea21adc6b420b2c43ad2602f27724},
doi = {10.3390/app112311272},
issn = {20763417},
year  = {2021},
date = {2021-01-01},
journal = {Applied Sciences (Switzerland)},
volume = {11},
number = {23},
publisher = {MDPI},
abstract = {Recently, from the deduction of the result MIP* = RE in quantum computation, it was obtained that Quantum Field Theory (QFT) allows for different forms of computation in quantum computers that Quantum Mechanics (QM) does not allow. Thus, there must exist forms of computation in the QFT representation of the Universe that the QM representation does not allow. We explain in a simple manner how the QFT representation allows for different forms of computation by describing the differences between QFT and QM, and obtain why the future of quantum computation will require the use of QFT. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
note = {cited By 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Oliveira20211109,
title = {OpenEHR modeling: improving clinical records during the COVID-19 pandemic},
author = {D. Oliveira and R. Miranda and P. Leuschner and N. Abreu and M. F. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105968830&doi=10.1007%2fs12553-021-00556-4&partnerID=40&md5=1c24a8159384e8caefc23ca6724b092d},
doi = {10.1007/s12553-021-00556-4},
issn = {21907188},
year  = {2021},
date = {2021-01-01},
journal = {Health and Technology},
volume = {11},
number = {5},
pages = {1109-1118},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The COVID-19 pandemic had put pressure on various national healthcare systems, due to the lack of health professionals and exhaustion of those avaliable, as well as lack of interoperability and inability to restructure their IT systems. Therefore, the restructuring of institutions at all levels is essential, especially at the level of their information systems. Furthermore, the COVID-19 pandemic had arrived in Portugal at March 2020, with a breakout on the northern region. In order to quickly respond to the pandemic, the CHUP healthcare institution, known as a research center, has embraced the challenge of developing and integrating a new approach based on the openEHR standard to interoperate with the institution’s existing information and its systems. An openEHR clinical modelling methodology was outlined and adopted, followed by a survey of daily clinical and technical requirements. With the arrival of the virus in Portugal, the CHUP institution has undergone through constant changes in their working methodologies as well as their openEHR modelling. As a result, an openEHR patient care workflow for COVID-19 was developed. © 2021, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature.},
note = {cited By 10},
keywords = {Article; coronavirus disease 2019; COVID-19 testing; data interoperability; data visualization; disease severity; electronic health record; human; medical information system; normal human; openEHR; pandemic; patient monitoring; patient referral; self monitoring; workflow},
pubstate = {published},
tppubtype = {article}
}
@article{Sousa2021,
title = {Hierarchical temporal memory theory approach to stock market time series forecasting},
author = {R. Sousa and T. Lima and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118847662&doi=10.3390%2felectronics10141630&partnerID=40&md5=76d11a862e1a7415e47811cad895a048},
doi = {10.3390/electronics10141630},
issn = {20799292},
year  = {2021},
date = {2021-01-01},
journal = {Electronics (Switzerland)},
volume = {10},
number = {14},
publisher = {MDPI},
abstract = {Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to recognize audio, image, and time series predictions, has helped to solve various types of problems. This paper aims to introduce a new theory, Hierarchical Temporal Memory (HTM), that applies to stock market prediction. HTM is based on the biological functions of the brain as well as its learning mechanism. The results are of significant relevance and show a low percentage of errors in the predictions made over time. It can be noted that the learning curve of the algorithm is fast, identifying trends in the stock market for all seven data universes using the same network. Although the algorithm suffered at the time a pandemic was declared, it was able to adapt and return to good predictions. HTM proved to be a good continuous learning method for predicting time series datasets. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
note = {cited By 8},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Sousa2021b,
title = {Software tools for conducting real-time information processing and visualization in industry: An up-to-date review},
author = {R. Sousa and R. Miranda and A. Moreira and C. Alves and N. Lori and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107330276&doi=10.3390%2fapp11114800&partnerID=40&md5=a3cf099cf99bbb7318471ee3259337ab},
doi = {10.3390/app11114800},
issn = {20763417},
year  = {2021},
date = {2021-01-01},
journal = {Applied Sciences (Switzerland)},
volume = {11},
number = {11},
publisher = {MDPI AG},
abstract = {The processing of information in real-time (through the processing of complex events) has become an essential task for the optimal functioning of manufacturing plants. Only in this way can artificial intelligence, data extraction, and even business intelligence techniques be applied, and the data produced daily be used in a beneficent way, enhancing automation processes and improving service delivery. Therefore, professionals and researchers need a wide range of tools to extract, transform, and load data in real-time efficiently. Additionally, the same tool supports or at least facilitates the visualization of this data intuitively and interactively. The review presented in this document aims to provide an up-to-date review of the various tools available to perform these tasks. Of the selected tools, a brief description of how they work, as well as the advantages and disadvantages of their use, will be presented. Furthermore, a critical analysis of overall operation and performance will be presented. Finally, a hybrid architecture that aims to synergize all tools and technologies is presented and discussed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
note = {cited By 11},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inbook{Esteves20211015,
title = {A proof of concept of a business intelligence platform to support the decision-making process of health professionals in waiting lists},
author = {M. Esteves and M. Esteves and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125154033&doi=10.4018%2f978-1-7998-9023-2.ch050&partnerID=40&md5=9577bb8f57d9f4a203f582aeab3e75e3},
doi = {10.4018/978-1-7998-9023-2.ch050},
isbn = {9781799890249; 9781799890232},
year  = {2021},
date = {2021-01-01},
journal = {Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering},
pages = {1015-1034},
publisher = {IGI Global},
abstract = {In the last years, the increase of the average waiting times in waiting lists has been an issue felt in several health institutions worldwide. Therefore, this problematic situation creates the need to define and implement new administrative measures in order to improve the management of these organizations. In this context, this research project arose in an attempt to support the decision-making process in waiting lists, namely medical appointments and surgeries, in a hospital located in the north of Portugal. Hereupon, a pervasive business intelligence platform was designed and developed using recent technologies such as React, Node.js, and MySQL. The proposed information technology artifact allows the efficient and easy identification in real-time of average waiting times outside the outlined patterns. Thus, the aim is to enable the reduction of average waiting times through the analysis of business intelligence indicators in order to ensure patients' satisfaction by taking necessary and adequate measures. © 2021, IGI Global.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@article{Carvalho20214,
title = {Health information systems (HIS) privacy restrictions for GDPR: Assessing initial impacts perceived by patients and healthcare professionals},
author = {M. Carvalho and P. Bandiera-Paiva and E. Marques and J. M. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103087726&doi=10.4018%2fIJRQEH.2021040102&partnerID=40&md5=d16580f5f169156ad1c8375313a943e8},
doi = {10.4018/IJRQEH.2021040102},
issn = {21609551},
year  = {2021},
date = {2021-01-01},
journal = {International Journal of Reliable and Quality E-Healthcare},
volume = {10},
number = {2},
pages = {4-16},
publisher = {IGI Global},
abstract = {The personal health information (PHI) that a health information system (HIS) stores and processes requires special caution to ensure authorized manipulation by system users. A diverse set of best practices, standards, and regulations are in place nowadays to achieve that purpose. To the access control element in a HIS, general data protection regulation (GDPR) will require explicit authorization and informed consent prior to this manipulation of patient information by healthcare practitioners in a system. The adaptations to cope this type of previous authorization on HIS requires not only a clear understanding of technicalities and modification to the underlying computational infrastructure but also the impact on players that interact with this type of system during healthcare service provision, namely patients and healthcare professionals. This article is an effort to understand this effect by means of collecting opinion from both players in a multicentric survey that presents different questions establishing scenarios that reflect this new control and its consequences. © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.},
note = {cited By 1},
keywords = {adult; article; health care personnel; human; information security; informed consent; medical information system; patient information; privacy},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Saragih2021,
title = {Sentiment Analysis of Social Media Twitter with Case of Large Scale Social Restriction in Jakarta using Support Vector Machine Algorithm},
author = {P. S. Saragih and D. Witarsyah and F. Hamami and J. M. MacHado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126541289&doi=10.1109%2fICADEIS52521.2021.9701961&partnerID=40&md5=4f324699bafcb89732b5245f423ef393},
doi = {10.1109/ICADEIS52521.2021.9701961},
isbn = {9781665437097},
year  = {2021},
date = {2021-01-01},
journal = {2021 International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2021},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {When the Large-Scale Social Restrictions (LSSR or PSBB in Indonesian) policy was implemented it the policy was not entirely obeyed by the community which then reaped various opinions and responses on various social media, especially on Twitter. This study aims to conduct a sentiment analysis to find out the cause or phenomena that occur based on the opinions or views of Twitter. The Tweet data about the implementation of LSSR both part 1 and part 2 in Jakarta were obtained as many as 1080 opinions using the crawling method then the data is manually labelled with two labels, which are positive and negative after labelled the data is cleaned after and the data is processed by being weighted using the Bag of Words and TF-IDF extraction feature. The classification process is carried out with four splitting data scenarios, with 60:40, 70:30, 80:20, 90:10 then classified using the Support Vector Machines algorithm. The final result of this study shows that the classification accuracy results using the Support Vector Machine algorithm with 90:10 data splitting ratio using the TFIDF extraction feature is superior with an accuracy value of 85.185% and F1-Score 72.413%, which is better when compared to the Bag of words extraction feature which produces an accuracy value of 83.333% and F1-Score 66.666%. As for this study, Twitter users tend to give opinions with negative sentiments, which contain complaints and discomfort regarding the implementation of the LSSR policies, both the first LSSR and the second LSSR. Finally, the results of this research are also expected to be input for the government when making better policies in the future. © 2021 IEEE.},
note = {cited By 0; Conference of 2021 International Conference Advancement in Data Science, E-learning and Information Systems, ICADEIS 2021 ; Conference Date: 13 October 2021 Through 14 October 2021; Conference Code:177133},
keywords = {Bag of words; Classification process; COVID-19; F1 scores; Jakarta; Large-scales; LSSR; Sentiment analysis; Social media; Support vector machines algorithms, Extraction; Social networking (online); Support vector machines, Sentiment analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Santos2021437,
title = {In-Car Violence Detection Based on the Audio Signal},
author = {F. Santos and D. Durães and F. S. Marcondes and N. Hammerschmidt and S. Lange and J. Machado and P. Novais},
editor = {Allmendinger R. Tino P. Camacho D.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126037019&doi=10.1007%2f978-3-030-91608-4_43&partnerID=40&md5=23842fca53bfddec1db51dc9346d55a4},
doi = {10.1007/978-3-030-91608-4_43},
issn = {03029743},
year  = {2021},
date = {2021-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13113 LNCS},
pages = {437-445},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {When it is intended to detect violence in the car, audio, speech processing, music, and ambient sound are some of the main points of this problem since it is necessary to find the similarities and differences between these domains. The recent increase in interest in deep learning has allowed practical applications in many areas of signal processing, often surpassing traditional signal processing on a large scale. This paper presents a comparative study of state-of-the-art deep learning architectures applied for inside car violence detection based only on the audio signal. The methodology proposed for audio signal representation was Mel-spectrogram, after an in-depth review of the literature. We build an In-Car video dataset in the experiments and apply four different deep learning architectures to solve the classification problem. The results have shown that the ResNet-18 model presents the best accuracy results on the test set. © 2021, Springer Nature Switzerland AG.},
note = {cited By 15; Conference of 22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021 ; Conference Date: 25 November 2021 Through 27 November 2021; Conference Code:269299},
keywords = {Action recognition; Ambient sounds; Audio action recognition; Audio signal; Audio violence detection; Deep learning; Large-scales; Learning architectures; Signal-processing; Violence detections, Audio acoustics, Classification (of information); Deep learning; Music; Signal detection; Speech processing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sousa2021209,
title = {Contactless Human-Computer Interaction Using a Deep Neural Network Pipeline for Real-Time Video Interpretation and Classification},
author = {R. Sousa and T. Jesus and V. Alves and J. Machado},
editor = {Santos M. F. Portela F. Guarda T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120534384&doi=10.1007%2f978-3-030-90241-4_17&partnerID=40&md5=c86fc35c9f2a3e50cd1b4e58b537b567},
doi = {10.1007/978-3-030-90241-4_17},
issn = {18650929},
year  = {2021},
date = {2021-01-01},
journal = {Communications in Computer and Information Science},
volume = {1485 CCIS},
pages = {209-220},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Nowadays, all applications are developed with the user’s comfort in mind. Regardless of the application’s objective, it should be as simple as possible so that it is easily accepted by its users. With the evolution of technology, simplicity has evolved and has become intrinsically related to the automation of tasks. Therefore, many researchers have focused their investigations on the interaction between humans and computing devices. However, this interaction is usually still carried out via a keyboard and/or a mouse. We present an essemble of deep neural networks for the detection and interpretation of gestural movement, in various environments. Its purpose is to introduce a new form of interaction between the human and computing devices in order to evolve this paradigm. The use case focused on detecting the movement of the user’s hands in real time and automatically interpreting the movement. © 2021, Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 1st International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2021 ; Conference Date: 25 November 2021 Through 27 November 2021; Conference Code:268849},
keywords = {Computer vision; Gesture recognition; Human computer interaction; Mammals, Computing devices; Contact less; Desktop task simulator; Evolution of technology; Hand-gesture recognition; New forms; Real time videos; Simple++; Video classification; Video interpretation, Deep neural networks},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Santos202165,
title = {Efficient Violence Detection Using Transfer Learning},
author = {F. Santos and D. Durães and F. S. Marcondes and S. Lange and J. Machado and P. Novais},
editor = {Duraes D. El Bolock A. De La Prieta F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116505876&doi=10.1007%2f978-3-030-85710-3_6&partnerID=40&md5=c229aac41b0bc0f030eb19d748792d78},
doi = {10.1007/978-3-030-85710-3_6},
issn = {18650929},
year  = {2021},
date = {2021-01-01},
journal = {Communications in Computer and Information Science},
volume = {1472 CCIS},
pages = {65-75},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In recent years several applications, namely in surveillance, human-computer interaction and video recovery based on its content has studied the detection and recognition of violence [22]. The purpose of violence detection is to automatically and effectively determine whether or not violence occurs in a short time. So, it is a crucial area since it will automatically enable the necessary means to stop the violence. To quickly solve this problem, we used models trained to solve general activity recognition problems such as Kinetics-400 to learn to extract general patterns that are very important to detect violent behaviour in videos. Our approach consists of using a state of the art pre-trained model in general activity recognition tasks (e.g. Kinetics-400) and then fine-tuning it to violence detection. We applied this approach in two violence datasets and achieved state-of-the-art results using only four input frames. © 2021, Springer Nature Switzerland AG.},
note = {cited By 4; Conference of International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2021 ; Conference Date: 6 October 2021 Through 9 October 2021; Conference Code:266119},
keywords = {Action recognition; Activity recognition; Deep learning; Fine tuning; General patterns; Learn+; State of the art; Transfer learning; Violence detections; Violent behavior, Deep learning, Human computer interaction; Pattern recognition; Security systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Durães2021290,
title = {Comparison of Transfer Learning Behaviour in Violence Detection with Different Public Datasets},
author = {D. Durães and F. Santos and F. S. Marcondes and S. Lange and J. Machado},
editor = {Lau N. Melo F.S. Marreiros G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115443718&doi=10.1007%2f978-3-030-86230-5_23&partnerID=40&md5=258a2451e74014f8e5df8c461bf232a0},
doi = {10.1007/978-3-030-86230-5_23},
issn = {03029743},
year  = {2021},
date = {2021-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {12981 LNAI},
pages = {290-298},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The detection and recognition of violence have been area of interest to research, mainly in surveillance, Human-Computer Interaction and information retrieval for video based on content. The primary purpose of detecting and recognizing violence is to automatically and in real-time recognize violence. Hence, it is a crucial area and object of several studies, as it will enable systems to have the necessary means to contain violence automatically. In this sense, pre-trained models are used to solve general problems of recognition of violent activity. These models were pre-trained with datasets from: hockey fight; movies; violence in real surveillance; and fighting in real situations. From this pre-training models, general patterns are extracted that are very important to detect violent behaviour in videos. Our approach uses a state-of-the-art pre-trained violence detection model in general activity recognition tasks and then tweaks it for violence detection inside a car. For this, we created our dataset with videos inside the car to apply in this study. © 2021, Springer Nature Switzerland AG.},
note = {cited By 3; Conference of 20th EPIA Conference on Artificial Intelligence, EPIA 2021 ; Conference Date: 7 September 2021 Through 9 September 2021; Conference Code:265179},
keywords = {Area of interest; Deep learning; Inside car; Learning behavior; Pre-training; Public dataset; Real situation; Real- time; Video recognition; Violence detections, Deep learning; Human computer interaction, Security systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Cunha2021156,
title = {A CRISP-DM Approach for Predicting Liver Failure Cases: An Indian Case Study},
author = {A. F. Cunha and D. Ferreira and C. Neto and A. Abelha and J. Machado},
editor = {Kalra J. Karwowski W. Ahram T.Z.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112285842&doi=10.1007%2f978-3-030-80624-8_20&partnerID=40&md5=6941c1bea4e3a230ddde5e6c03b7102b},
doi = {10.1007/978-3-030-80624-8_20},
issn = {23673370},
year  = {2021},
date = {2021-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {271},
pages = {156-164},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {We are currently living a period in which data processing and analysis are increasingly relevant and the health sector is no exception. In this way, through data mining processes, it is possible to make a number of predictions in the medical field, such as predicting medical conditions and disease progression. Acute Liver Failure (ALF) is a rare but critical disorder associated with high mortality. The aim of this work is to predict cases of acute hepatic insufficiency based on clinical data through data mining techniques. To this end, the CRISP-DM methodology was followed, in which five classifiers were applied, namely, Decision Tree, k-Nearest Neighbor, Random Forest, Rule Induction, and Naïve Bayes. Throughout this work, the RapidMiner software was used and the different models were analyzed based on Accuracy, Precision, Recall, Kappa Statistic, and Specificity. The best data mining model achieved an Accuracy of 0.925, a Precision of 0.869, a Recall of 1.000, a Kappa of 0.849, and a Specificity of 0.849, using split validation and the k-Nearest Neighbor algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 1; Conference of AHFE Conferences on Human Factors in Software and Systems Engineering, Artificial Intelligence and Social Computing, and Energy, 2021 ; Conference Date: 25 July 2021 Through 29 July 2021; Conference Code:262519},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neto2021287,
title = {Step Towards Predicting Patient Length of Stay in Intensive Care Units},
author = {C. Neto and G. Pontes and A. Domente and F. Reinolds and J. Costa and D. Ferreira and J. Machado},
editor = {Dzemyda G. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107362075&doi=10.1007%2f978-3-030-72654-6_28&partnerID=40&md5=aae7622245688001b9d7dde10eb6d271},
doi = {10.1007/978-3-030-72654-6_28},
issn = {21945357},
year  = {2021},
date = {2021-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1368 AISC},
pages = {287-297},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Increasingly, hospitals are collecting huge amounts of data through new storage methods. These data can be use to extract hidden knowledge, which can be crucial to estimate the length of stay of admitted patients in order to improve the management of hospital resources. Hence, this article portrays the performance analysis of different data mining techniques through the application of learning algorithms in order to predict patients’ length of stay when admitted to an Intensive Care Unit. The data used in this study contains about 60,000 records and 28 features with personal and medical information. A full analysis of the results obtained with different Machine Learning algorithms showed that the model trained with the Gradient Boosted Trees algorithm and using only the features that were strongly correlated to the patient’s length of stay, achieved the best performance with 99,19% of accuracy. In this sense, an accurate understanding of the factors associated with the length of stay in intensive care units was achieved. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of World Conference on Information Systems and Technologies, WorldCIST 2021 ; Conference Date: 30 March 2021 Through 2 April 2021; Conference Code:256979},
keywords = {Data mining; Digital storage; Information management; Information systems; Information use; Intensive care units; Machine learning; Trees (mathematics), Hidden knowledge; Length of stay; Medical information; Performance analysis; Trees algorithm, Learning algorithms},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2021893,
title = {Steps towards an healthcare information model based on openEHR},
author = {D. Oliveira and R. Miranda and F. Hak and N. Abreu and P. Leuschner and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106726178&doi=10.1016%2fj.procs.2021.04.015&partnerID=40&md5=b476415f1452184e4a7bafbde7e6700d},
doi = {10.1016/j.procs.2021.04.015},
issn = {18770509},
year  = {2021},
date = {2021-01-01},
journal = {Procedia Computer Science},
volume = {184},
pages = {893-898},
publisher = {Elsevier B.V.},
abstract = {During COVID-19 pandemic crisis, healthcare institutions globally were experiencing a VUCA - Volatile, Uncertain, Complex, and Ambiguous - environment. Efficient clinical and administrative management had never been so emergent. To achieve this goal, different components of the Healthcare Information System (HIS) must cooperate and interoperate flawlessly. Data standardization is a necessary step towards normalization and interoperability between existing Legacy Systems (LSs), and provides for longitudinal, highly reliable and persistent Electronic Health Records (EHRs). The openEHR standard was chosen for its overall dual domain architecture, where the more dynamic clinical information model may evolve independently from the relatively stable Reference Model (RM). Its Information Model (IM) comprises demographic, administrative and clinical systems. Critical clinical terms have been aligned to the FHIR HL7 standard, as to further support interoperability. © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)},
note = {cited By 4; Conference of 12th International Conference on Ambient Systems, Networks and Technologies, ANT 2021 / 4th International Conference on Emerging Data and Industry 4.0, EDI40 2021 / Affiliated Workshops ; Conference Date: 23 March 2021 Through 26 March 2021; Conference Code:145658},
keywords = {Clinical information model; Electronic health; Elsevier; Health records; Healthcare information system; Information models; Model-based OPC; Open Access; OpenEHR; Reference-models, Health care; Information management; Information systems; Information theory; Information use; Interoperability; Legacy systems; Medical computing, Records management},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Chaves2021917,
title = {Development of FHIR based web applications for appointment management in healthcare},
author = {A. Chaves and T. Guimarães and J. Duarte and H. Peixoto and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106686824&doi=10.1016%2fj.procs.2021.03.114&partnerID=40&md5=a0681e0e706d76a995e932f18cff4ba2},
doi = {10.1016/j.procs.2021.03.114},
issn = {18770509},
year  = {2021},
date = {2021-01-01},
journal = {Procedia Computer Science},
volume = {184},
pages = {917-922},
publisher = {Elsevier B.V.},
abstract = {The integration of Information Technology systems in healthcare is no new concept, however, the ever growing solutions offered by the IT field are pushing a revamp of older implementations of Hospital Information Systems. Contemporary web-based solutions are now readily available and promise independence from operating systems and desktop bound systems, while incorporating faster and more secure methods. The focus on interoperable systems has been setting new goals towards fully computerized hospital management and the progress of healthcare standards over the years has made interoperability an obligation. The work presented hereby reflects a FHIR web based application to overcome the problem presented by scheduling and appointment management. © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)},
note = {cited By 3; Conference of 12th International Conference on Ambient Systems, Networks and Technologies, ANT 2021 / 4th International Conference on Emerging Data and Industry 4.0, EDI40 2021 / Affiliated Workshops ; Conference Date: 23 March 2021 Through 26 March 2021; Conference Code:145658},
keywords = {Elsevier; FHIR; Hospital information systems; Information technology systems; Open Access; Schedule management; WEB application; Web applications; Web development; Web-based solutions, Health care; Hospitals; Information management; Information systems; Information use; Scheduling; Websites, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Santos2021104,
title = {Modelling a Deep Learning Framework for Recognition of Human Actions on Video},
author = {F. Santos and D. Durães and F. Marcondes and M. Gomes and F. Gonçalves and J. Fonseca and J. Wingbermuehle and J. Machado and P. Novais},
editor = {Dzemyda G. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105949815&doi=10.1007%2f978-3-030-72657-7_10&partnerID=40&md5=63526ae46868c827835600c4dba3711b},
doi = {10.1007/978-3-030-72657-7_10},
issn = {21945357},
year  = {2021},
date = {2021-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1365 AIST},
pages = {104-112},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In Human action recognition, the identification of actions is a system that can detect human activities. The types of human activity are classified into four different categories, depending on the complexity of the steps and the number of body parts involved in the action, namely gestures, actions, interactions, and activities [1]. It is challenging for video Human action recognition to capture useful and discriminative features because of the human body's variations. To obtain Intelligent Solutions for action recognition, it is necessary to training models to recognize which action is performed by a person. This paper conducted an experience on Human action recognition compare several deep learning models with a small dataset. The main goal is to obtain the same or better results than the literature, which apply a bigger dataset with the necessity of high-performance hardware. Our analysis provides a roadmap to reach the training, classification, and validation of each model. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 3; Conference of World Conference on Information Systems and Technologies, WorldCIST 2021 ; Conference Date: 1 April 2021 Through 2 April 2021; Conference Code:256979},
keywords = {Action recognition; Discriminative features; High-performance hardware; Human activities; Human-action recognition; Intelligent solutions; Learning frameworks; Learning models, Deep learning, Information systems; Information use; Learning systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Vieira2021511,
title = {Data Mining Approach to Classify Cases of Lung Cancer},
author = {E. Vieira and D. Ferreira and C. Neto and A. Abelha and J. Machado},
editor = {Dzemyda G. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105949008&doi=10.1007%2f978-3-030-72657-7_49&partnerID=40&md5=071cf3523dd9f477acf20273d8405f0e},
doi = {10.1007/978-3-030-72657-7_49},
issn = {21945357},
year  = {2021},
date = {2021-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1365 AIST},
pages = {511-521},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {According to the World Cancer Research Fund, a leading authority on cancer prevention research, lung cancer is the most commonly occurring cancer in men and the third most commonly occurring cancer in women, with the 5-year relative survival percentage being significantly low. Smoking is the major risk factor for lung cancer and the symptoms associated with it include cough, fatigue, shortness of breath, chest pain, weight loss, and loss of appetite. In an attempt to build a model capable of identifying individuals with lung cancer, this study aims to build a data mining classification model to predict whether or not a patient has lung cancer based on crucial features such as the above mentioned symptoms. Through the CRISP-DM methodology and the RapidMiner software, different models were built, using different scenarios, algorithms, sampling methods, and data approaches. The best data mining model achieved an accuracy of 93%, a sensitivity of 96%, a specificity of 90% and a precision of 91%, using the Artificial Neural Network algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 3; Conference of World Conference on Information Systems and Technologies, WorldCIST 2021 ; Conference Date: 1 April 2021 Through 2 April 2021; Conference Code:256979},
keywords = {Artificial neural network algorithm; Cancer prevention; Cancer research; Data mining models; Mining classification; Risk factors; Sampling method; Weight loss, Biological organs; Classification (of information); Diseases; Information systems; Information use; Neural networks, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neto2021210,
title = {Prediction Models for Polycystic Ovary Syndrome Using Data Mining},
author = {C. Neto and M. Silva and M. Fernandes and D. Ferreira and J. Machado},
editor = {Antipova T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103521052&doi=10.1007%2f978-3-030-71782-7_19&partnerID=40&md5=cd8917667f9e56f1982c1aba9ff64f02},
doi = {10.1007/978-3-030-71782-7_19},
issn = {21945357},
year  = {2021},
date = {2021-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1352},
pages = {210-221},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Polycystic Ovary Syndrome is an endocrine abnormality that occurs in the female reproductive system and is considered a heterogeneous disorder because of the different criteria used for its diagnosis. Early detection and treatment are critical factors to reduce the risk of long-term complications, such as type 2 diabetes and heart disease. With the vast amount of data being collected daily in healthcare environments, it is possible to build Decision Support Systems using Data Mining and Machine Learning. Currently, healthcare systems have advanced skills like Artificial Intelligence, Machine Learning and Data Mining to offer intelligent and expert healthcare services. The use of efficient Data Mining techniques is able to reveal and extract hidden information from clinical and laboratory patient data, which can be helpful to assist doctors in maximizing the accuracy of the diagnosis. In this sense, this paper aims to predict, using the classification techniques and the CRISP-DM methodology, the presence of Polycystic Ovary Syndrome. This paper compares the performance of multiple algorithms, namely, Support Vector Machines, Multilayer Perceptron Neural Network, Random Forest, Logistic Regression and Gaussian Naïve Bayes. In the end, it was found that Random Forest provides the best classification, and the use of data sampling techniques also improves the results, allowing to achieve a sensitivity of 0.94, an accuracy of 0.95, a precision of 0.96 and a specificity of 0.96. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 8; Conference of International Conference on Advances in Digital Science, ICADS 2021 ; Conference Date: 19 February 2021 Through 21 February 2021; Conference Code:256499},
keywords = {Classification technique; Healthcare environments; Healthcare services; Hidden information; Multi-layer perceptron neural networks; Multiple algorithms; Polycystic ovary syndromes; Reproductive systems, Data mining, Decision support systems; Decision trees; Diagnosis; Diseases; Health care; Hospital data processing; Learning systems; Logistic regression; Multilayer neural networks; Predictive analytics; Random forests; Support vector machines; Support vector regression},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Abreu2021198,
title = {Diagnosis of Diabetic Retinopathy Using Data Mining Classification Techniques},
author = {A. Abreu and D. Ferreira and C. Neto and A. Abelha and J. Machado},
editor = {Antipova T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103501921&doi=10.1007%2f978-3-030-71782-7_18&partnerID=40&md5=00cbc654502f69e638aa251a4aad2b0f},
doi = {10.1007/978-3-030-71782-7_18},
issn = {21945357},
year  = {2021},
date = {2021-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1352},
pages = {198-209},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Diabetic retinopathy is one of the complications of diabetes that affects the small vessels of the retina, being the main cause of blindness in adults. An early detection of this disease is essential, as it can prevent blindness as well as other irreversible harmful outcomes. This article attempts to develop a data mining model capable of identifying diabetic retinopathy in patients based on features extracted from eye fundus images. The data mining process was carried out in the RapidMiner software and followed the CRISP-DM methodology. In particular, classification models were built by combining different scenarios, algorithms, and sampling methods. The data mining model which performed best achieved an accuracy of 76.90%, a precision of 85.92%, and a sensitivity of 67.40%, using the Logistic Regression algorithm and Split Validation as the sampling method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 2; Conference of International Conference on Advances in Digital Science, ICADS 2021 ; Conference Date: 19 February 2021 Through 21 February 2021; Conference Code:256499},
keywords = {Classification models; Data mining models; Data mining process; Diabetic retinopathy; Eye fundus; Logistic regression algorithms; Mining classification; Sampling method, Computer aided diagnosis; Eye protection; Logistic regression; Medical imaging, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Neto2021,
title = {Different Scenarios for the Prediction of Hospital Readmission of Diabetic Patients},
author = {C. Neto and F. Senra and J. Leite and N. Rei and R. Rodrigues and D. Ferreira and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098865327&doi=10.1007%2fs10916-020-01686-4&partnerID=40&md5=3c6725a7ffa1a4f6d6a0a99c3990de9d},
doi = {10.1007/s10916-020-01686-4},
issn = {01485598},
year  = {2021},
date = {2021-01-01},
journal = {Journal of Medical Systems},
volume = {45},
number = {1},
publisher = {Springer},
abstract = {Hospitals generate large amounts of data on a daily basis, but most of the time that data is just an overwhelming amount of information which never transitions to knowledge. Through the application of Data Mining techniques it is possible to find hidden relations or patterns among the data and convert those into knowledge that can further be used to aid in the decision-making of hospital professionals. This study aims to use information about patients with diabetes, which is a chronic (long-term) condition that occurs when the body does not produce enough or any insulin. The main purpose is to help hospitals improve their care with diabetic patients and consequently reduce readmission costs. An hospital readmission is an episode in which a patient discharged from a hospital is admitted again within a specified period of time (usually a 30 day period). This period allows hospitals to verify that their services are being performed correctly and also to verify the costs of these re-admissions. The goal of the study is to predict if a patient who suffers from diabetes will be readmitted, after being discharged, using Machine Leaning algorithms. The final results revealed that the most efficient algorithm was Random Forest with 0.898 of accuracy. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.},
note = {cited By 11},
keywords = {acarbose; acetohexamide; chlorpropamide; glibenclamide; glimepiride; glipizide; insulin; metformin; miglitol; nateglinide; pioglitazone; repaglinide; rosiglitazone; tolazamide; tolbutamide; troglitazone, Algorithms; Data Mining; Diabetes Mellitus; Humans; Patient Discharge; Patient Readmission; Retrospective Studies; Risk Factors},
pubstate = {published},
tppubtype = {article}
}
@article{Martins2021,
title = {Data Mining for Cardiovascular Disease Prediction},
author = {B. Martins and D. Ferreira and C. Neto and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098784234&doi=10.1007%2fs10916-020-01682-8&partnerID=40&md5=fcd257d4ecb8dbe046bdb892e8995046},
doi = {10.1007/s10916-020-01682-8},
issn = {01485598},
year  = {2021},
date = {2021-01-01},
journal = {Journal of Medical Systems},
volume = {45},
number = {1},
publisher = {Springer},
abstract = {Cardiovascular diseases (CVDs) aredisorders of the heart and blood vessels and are a major cause of disability and premature death worldwide. Individuals at higher risk of developing CVD must be noticed at an early stage to prevent premature deaths. Advances in the field of computational intelligence, together with the vast amount of data produced daily in clinical settings, have made it possible to create recognition systems capable of identifying hidden patterns and useful information. This paper focuses on the application of Data Mining Techniques (DMTs) to clinical data collected during the medical examination in an attempt to predict whether or not an individual has a CVD. To this end, the CRossIndustry Standard Process for Data Mining (CRISP-DM) methodology was followed, in which five classifiers were applied, namely DT, Optimized DT, RI, RF, and DL. The models were mainly developed using the RapidMiner software with the assist of the WEKA tool and were analyzed based on accuracy, precision, sensitivity, and specificity. The results obtained were considered promising on the basis of the research for effective means of diagnosing CVD, with the best model being Optimized DT, which achieved the highest values for all the evaluation metrics, 73.54%, 75.82%, 68.89%, 78.16% and 0.788 for accuracy, precision, sensitivity, specificity, and AUC, respectively. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.},
note = {cited By 39},
keywords = {artificial intelligence; cardiovascular disease; data mining; human, Artificial Intelligence; Cardiovascular Diseases; Data Mining; Humans},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Castanheira202144,
title = {Overcoming challenges in healthcare interoperability regulatory compliance},
author = {A. Castanheira and H. Peixoto and J. Machado},
editor = {Larriba-Pey J. L. Vercelli G. Novais P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091511192&doi=10.1007%2f978-3-030-58356-9_5&partnerID=40&md5=74a0dcb5fadb890331a3a47d465a3e22},
doi = {10.1007/978-3-030-58356-9_5},
issn = {21945357},
year  = {2021},
date = {2021-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1239 AISC},
pages = {44-53},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {There has been a significant increase in the quantity of information stored digitally by health institutions. Such information contains personal data from the actors in their universe. Thus, is crucial that it is governed by a set of rules, in order to allow it to be understood without losing important data. With the increased use of digital tools for storing and exchanging information, ethical issues began to arise in the context of the privacy of personal data. Questions about access, processing, treatment and storage of personal data became increasingly important in society, leading to the creation of the General Data Protection Regulation (GDPR) in force at European level. GDPR is one of the main challenges in healthcare interoperability regulatory compliance, therefore the proposed architecture shows an approach to enforce GDPR compliance into Agency for Integration, Diffusion and Archive Platform (AIDA), which is held by several healthcare unities in Portugal, using technologies like ElasticSearch and Kibana. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.},
note = {cited By 2; Conference of 11th International Symposium on Ambient Intelligence, ISAmI 2020 ; Conference Date: 7 October 2020 Through 9 October 2020; Conference Code:245169},
keywords = {Ambient intelligence, Application programs; Artificial intelligence; Data privacy; Digital devices; Digital storage; Health care; Interoperability; Regulatory compliance, Digital tools; Ethical issues; European levels; General data protection regulations; Healthcare Interoperability; Portugal; Proposed architectures; Set of rules},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Durães2021106,
title = {Detection violent behaviors: A survey},
author = {D. Durães and F. S. Marcondes and F. Gonçalves and J. Fonseca and J. Machado and P. Novais},
editor = {Larriba-Pey J. L. Vercelli G. Novais P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091503158&doi=10.1007%2f978-3-030-58356-9_11&partnerID=40&md5=ed2f6cb833a6cc4d92e3dca5aabec4c0},
doi = {10.1007/978-3-030-58356-9_11},
issn = {21945357},
year  = {2021},
date = {2021-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1239 AISC},
pages = {106-116},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Violence detection behavior is a particular problem regarding the great problem action recognition. In recent years, the detection and recognition of violence has been studied for several applications, namely in surveillance. In this paper, we conducted a recent systematic review of the literature on this subject, covering a selection of various researched papers. The selected works were classified into three main approaches for violence detection: video, audio, and multimodal audio and video. Our analysis provides a roadmap to guide future research to design automatic violence detection systems. Techniques related to the extraction and description of resources to represent behavior are also reviewed. Classification methods and structures for behavior modelling are also provided. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.},
note = {cited By 13; Conference of 11th International Symposium on Ambient Intelligence, ISAmI 2020 ; Conference Date: 7 October 2020 Through 9 October 2020; Conference Code:245169},
keywords = {Action recognition; Audio and video; Behavior modelling; Classification methods; Multi-modal; Systematic Review; Violence detections; Violent behavior, Ambient intelligence, Application programs; Artificial intelligence; Classification (of information)},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marcondes2021211,
title = {In-vehicle violence detection in carpooling: A brief survey towards a general surveillance system},
author = {F. S. Marcondes and D. Durães and F. Gonçalves and J. Fonseca and J. Machado and P. Novais},
editor = {Matsui K. Herrera-Viedma E. Dong Y.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089722186&doi=10.1007%2f978-3-030-53036-5_23&partnerID=40&md5=d4ebacd935c0a98d12558df672ef3b1f},
doi = {10.1007/978-3-030-53036-5_23},
issn = {21945357},
year  = {2021},
date = {2021-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1237 AISC},
pages = {211-220},
publisher = {Springer},
abstract = {Violence is a word that encompasses several meanings ranging from an actual fight to theft and several types of harassment. Therefore, violence detection through surveillance systems can be a quite difficult yet important task. The increasing use of carpooling services and vehicle sharing brought the need to implement a sufficient general surveillance system for monitoring these vehicles for assuring the passengers’ safety during the ride. This paper raised the literature for this matter, finding fewer research papers than it was expected for the in-vehicle perspective, noticeably to sexual harassment. Most of the research papers focused on out-vehicle issues such as runs over and vehicle theft. In-vehicle electronic components security and cockpit user experience were perceived as major concern areas. This paper discusses these findings and presents some insights about in-vehicle surveillance. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.},
note = {cited By 14; Conference of 17th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2020 ; Conference Date: 17 June 2020 Through 19 June 2020; Conference Code:243089},
keywords = {Artificial intelligence; Crime; Monitoring; Security systems; User experience, General Surveillance; Research papers; Sexual harassment; Surveillance systems; Vehicle electronics; Vehicle surveillances; Violence detections, Vehicles},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Ferreira2020,
title = {Recommendation system using autoencoders},
author = {D. Ferreira and S. Silva and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089725316&doi=10.3390%2fapp10165510&partnerID=40&md5=9e9df890fc4543a493e0be83221f7287},
doi = {10.3390/app10165510},
issn = {20763417},
year  = {2020},
date = {2020-01-01},
journal = {Applied Sciences (Switzerland)},
volume = {10},
number = {16},
publisher = {MDPI AG},
abstract = {The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one. © 2020 by the authors.},
note = {cited By 40},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Pereira202034,
title = {The clinical informatization in Portugal an approach to the national health service certification},
author = {S. Pereira and L. Silva and J. Machado and A. Cabral},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092591116&doi=10.4018%2fIJRQEH.2020040103&partnerID=40&md5=b2ed4b8fa5af28188056e0745d91e70d},
doi = {10.4018/IJRQEH.2020040103},
issn = {21609551},
year  = {2020},
date = {2020-01-01},
journal = {International Journal of Reliable and Quality E-Healthcare},
volume = {9},
number = {2},
pages = {34-47},
publisher = {IGI Global},
abstract = {In the context of the Technological Revolution, people are forced to change their way of being in order to survive in an increasingly competitive and efficient society. The healthcare sector is no exception. The clinical informatization brought a lot of changes in procedures and ways to act and manage in order to follow the advent of the Information Age. However, this clinical informatization should be evaluated and measured in order to report the actual stage of dematerialization and identify possible improvements. The maturity models, such as the EMRAM model, are good candidates to reach these goals. On behalf of the Health Ministry, the Portuguese Shared Services of the Ministry of Health wanted to implement the model in the National Health Service to certify, at a clinical level, the institutions, and, at the same time, contribute with a new methodology to ensure the certification of administrative services of health institutions. © 2020 International Journal of Abdominal Wall and Hernia Surgery. All rights reserved.},
note = {cited By 7},
keywords = {article; certification; data mining; health care cost; human; maturity; national health service; Portugal},
pubstate = {published},
tppubtype = {article}
}
@inbook{Portela2020213,
title = {Step towards pervasive technology assessment in intensive medicine},
author = {F. Portela and M. F. Santos and J. Machado and A. Silva Abelha and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131619029&doi=10.4018%2f978-1-7998-2451-0.ch012&partnerID=40&md5=a88c4bf3896fbddcbf6dba6cac057813},
doi = {10.4018/978-1-7998-2451-0.ch012},
isbn = {9781799824527; 9781799824510},
year  = {2020},
date = {2020-01-01},
journal = {Hospital Management and Emergency Medicine: Breakthroughs in Research and Practice},
pages = {213-229},
publisher = {IGI Global},
abstract = {This paper presents the evaluation of a Pervasive Intelligent Decision Support System in Intensive Medicine making use of Technology Acceptance Model 3 (TAM3). Two rounds of questionnaires were distributed and compared. The work is based on a discursive evaluation of a method employed to assess a new and innovative technology (INTCare) using the four constructs of TAM3 and statistical metrics. The paper crosses the TAM3 constructs with INTCare features to produce a questionnaire to provide a better comprehension of the users' intentions. The final results are essential to validate the system and understand the user sensitivity. The paper validates a method to access technologies in critical environments and shows an example of how a questionnaire can be developed based on TAM3. It also proves the viability of using this method and advises that two rounds of questionnaires should be performed if we want to have better evidence on user satisfaction. © 2020, IGI Global.},
note = {cited By 2},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Veloso202084,
title = {Categorize readmitted patients in intensive medicine by means of clustering data mining},
author = {R. Veloso and F. Portela and M. F. Santos and J. Machado and A. S. Abelha and F. Rua and Á. Silva},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131614785&doi=10.4018%2f978-1-7998-2451-0.ch005&partnerID=40&md5=c2c24672e7d790c48c977a5abefea98f},
doi = {10.4018/978-1-7998-2451-0.ch005},
isbn = {9781799824527; 9781799824510},
year  = {2020},
date = {2020-01-01},
journal = {Hospital Management and Emergency Medicine: Breakthroughs in Research and Practice},
pages = {84-99},
publisher = {IGI Global},
abstract = {With a constant increasing in the health expenses and the aggravation of the global economic situation, managing costs and resources in healthcare is nowadays an essential point in the management of hospitals. The goal of this work is to apply clustering techniques to data collected in real-time about readmitted patients in Intensive Care Units in order to know some possible features that affect read-missions in this area. By knowing the common characteristics of readmitted patients it will be possible helping to improve patient outcome, reduce costs and prevent future readmissions. In this study, it was followed the Stability and Workload Index for Transfer (SWIFT) combined with the results of clinical tests for substances like lactic acid, leucocytes, bilirubin, platelets and creatinine. Attributes like sex, age and identification if the patient came from the chirurgical block were also considered in the characterization of potential readmissions. In general, all the models presented very good results being the Davies-Bouldin index lower than 0.82, where the best index was 0.425. © 2020, IGI Global.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Portela2020112,
title = {Data quality and critical events in ventilation: An intensive care study},
author = {F. Portela and M. F. Santos and A. S. Abelha and J. Machado and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131610829&doi=10.4018%2f978-1-7998-2451-0.ch007&partnerID=40&md5=d9bc5d42b6777095e6da21a0c8d8a94c},
doi = {10.4018/978-1-7998-2451-0.ch007},
isbn = {9781799824527; 9781799824510},
year  = {2020},
date = {2020-01-01},
journal = {Hospital Management and Emergency Medicine: Breakthroughs in Research and Practice},
pages = {112-121},
publisher = {IGI Global},
abstract = {The data quality assessment is a critical task in Intensive Care Units (ICUs). In the ICUs the patients are continuously monitored and the values are collected in real-time through data streaming processes. In the case of ventilation, the ventilator is monitoring the patient respiratory system and then a gateway receives the monitored values. This process can collect any values, noise values or values that can have clinical significance, for example, when a patient is having a critical event associated with the respiratory system. In this paper, the critical events concept was applied to the ventilation system, and a quality assessment of the collected data was performed when a new value arrived. Some interesting results were achieved: 56.59% of the events were critical, and 5% of the data collected were noise values. In this field, Average Ventilation Pressure and Peak flow are respectively the variables with the most influence. © 2020, IGI Global.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Braga2020567,
title = {Applied pervasive patient timeline in intensive care units},
author = {A. Braga and F. Portela and M. F. Santos and A. SilvaAbelha and J. Machado and Á. Silva and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131609614&doi=10.4018%2f978-1-7998-2451-0.ch028&partnerID=40&md5=8ef67d9af8c8a77435383f6ac737acd5},
doi = {10.4018/978-1-7998-2451-0.ch028},
isbn = {9781799824527; 9781799824510},
year  = {2020},
date = {2020-01-01},
journal = {Hospital Management and Emergency Medicine: Breakthroughs in Research and Practice},
pages = {567-579},
publisher = {IGI Global},
abstract = {This study has the objective of introducing an innovative way of presenting and representing information concerning patients in Intensive Care Units. Therefore, the Pervasive Patient Timeline, which has the purpose of offering support to intensivists' decision-making process, by providing access to a real-time environment, was developed. The solution is patient-centred as it can be accessed from anywhere, at any time and it contains patients' clinical data since they are admitted to the ICU until their discharge. The environment holds data concerning vital signs, laboratory results, therapeutics, and data mining predictions, which can be analysed to have a better understanding of patients' present and future condition. Due to the nature of the critical care environment, the pervasive aspect is crucial because it allows intensivists make decisions when they have to be made. The Pervasive Patient Timeline is focused on improving the quality of care by helping the intensivists perform better in their daily activity. © 2020, IGI Global.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Pinto2020562,
title = {Data mining to predict early stage chronic kidney disease},
author = {A. Pinto and D. Ferreira and C. Neto and A. Abelha and J. Machado},
editor = {Yasar A. Shakshuki E.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099880679&doi=10.1016%2fj.procs.2020.10.079&partnerID=40&md5=3e496eba589d1580b145c7043e27343e},
doi = {10.1016/j.procs.2020.10.079},
issn = {18770509},
year  = {2020},
date = {2020-01-01},
journal = {Procedia Computer Science},
volume = {177},
pages = {562-567},
publisher = {Elsevier B.V.},
abstract = {Chronic Kidney Disease (CKD) is a condition characterized by a gradual loss of kidney function over time. In national and international guidelines, CKD is organized into different degrees of risk stratification using commonly available markers. It is usually asymptomatic in its early stages, and early detection is important to reduce future risks. This study used the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and the WEKA software to build a system that can classify the chronic condition of kidney disease based on accuracy, sensitivity, specificity and precision. The results obtained were considered satisfactory, achieving the most suitable result of 97.66% of accuracy, 96.13% of sensitivity, 98.78% of specificity and 98.31% of precision with the J48 algorithm. © 2020 The Authors. Published by Elsevier B.V.},
note = {cited By 5; Conference of 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020 ; Conference Date: 2 November 2020 Through 5 November 2020; Conference Code:166555},
keywords = {Chronic conditions; Chronic kidney disease; CRISP-DM; Cross industry; Kidney disease; Kidney function; Risk stratification, Computer science; Computers, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gonçalves2020556,
title = {Prediction of mental illness associated with unemployment using data mining},
author = {C. Gonçalves and D. Ferreira and C. Neto and A. Abelha and J. Machado},
editor = {Yasar A. Shakshuki E.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099879347&doi=10.1016%2fj.procs.2020.10.078&partnerID=40&md5=077569e0b99ba95909110a8699069c1e},
doi = {10.1016/j.procs.2020.10.078},
issn = {18770509},
year  = {2020},
date = {2020-01-01},
journal = {Procedia Computer Science},
volume = {177},
pages = {556-561},
publisher = {Elsevier B.V.},
abstract = {Mental illness is a concern these days, affecting people worldwide and across all kinds of ages. This article aims to predict mental illness and discover its association with unemployment as well as other possible causes behind the illness. In order to accomplish this goal, a Data Mining (DM) process was performed using the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology and the RapidMiner Studio software. In the end, the results obtained were considered promising since all the evaluation metrics, namely accuracy, sensitivity, and specificity, obtained values above 90%. The study also allowed, in the end, to identify the factors associated with the prediction of mental illness. © 2020 The Authors. Published by Elsevier B.V.},
note = {cited By 7; Conference of 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020 ; Conference Date: 2 November 2020 Through 5 November 2020; Conference Code:166555},
keywords = {CRISP-DM; Cross industry; Evaluation metrics; Mental illness, Data mining, Diseases; Employment; Forecasting},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2020522,
title = {Management of a pandemic based on an openEHR approach},
author = {D. Oliveira and R. Miranda and N. Abreu and P. Leuschner and A. Abelha and M. Santos and J. Machado},
editor = {Yasar A. Shakshuki E.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099877973&doi=10.1016%2fj.procs.2020.10.072&partnerID=40&md5=3da6d21c32322c5623b9f10c2a037f49},
doi = {10.1016/j.procs.2020.10.072},
issn = {18770509},
year  = {2020},
date = {2020-01-01},
journal = {Procedia Computer Science},
volume = {177},
pages = {522-527},
publisher = {Elsevier B.V.},
abstract = {The COVID-19 pandemic has collapsed several national health systems, due to the lack of healthcare professionals and exhaustion of those employed, as well as the lack of interoperability and capacity to restructure their informatic systems. Therefore, the restructuring of institutions at all levels is essential, mainly at the level of their Information Systems. When the COVID-19 pandemic had spread to Portugal in March 2020, with a breakout on the northern region, the Centro Hospitalar Universitário do Porto (CHUP) healthcare institution had felt the need to develop and integrate a new approach based on the openEHR standard to interoperate with the institution's existing information systems, with the aim of responding quickly to the pandemic's evolution. © 2020 The Authors. Published by Elsevier B.V.},
note = {cited By 11; Conference of 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020 ; Conference Date: 2 November 2020 Through 5 November 2020; Conference Code:166555},
keywords = {Health care professionals; Healthcare institutions; National health system; New approaches; Northern regions; Portugal, Health care; Information systems; Information use; Medical computing, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Peixoto2020209,
title = {Integrating a Data Mining Engine into Recommender Systems},
author = {V. Peixoto and H. Peixoto and J. Machado},
editor = {Camacho D. Novais P. Analide C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097363571&doi=10.1007%2f978-3-030-62362-3_19&partnerID=40&md5=742581dbe55b54d9a6d34bd6fbc8e28a},
doi = {10.1007/978-3-030-62362-3_19},
issn = {03029743},
year  = {2020},
date = {2020-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {12489 LNCS},
pages = {209-220},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {History could be epitomised to a handful of events that changed the course of human evolution. Now, we found ourselves amid another revolution: the data revolution. Easily unnoticeable, this new outlook is shifting in every possible way how we interact with the internet and, for the first time in history, how the internet interacts with us. This new kind of interactions is defined by connections between users and consumable goods (products, articles, movies, etc.). And through these connections, knowledge can be found. This is the definition of data mining. Buying online has become mainstream due to its convenience and variety, but the enormous offering options affect negatively the user experience. Millions of products are displayed online, and frequently the search for the craved product is long and tiring. This process can lead to a loss of interest from the customers and, consequentially, losing profits. The competition is increasing, and personalisation is considered the game-changer for platforms. This article follows the research and implementation of a recommender engine in a well-known Portuguese e-commerce platform specialised in clothing and sports apparel, aiming the increase in customer engagement, by providing a personalised experience with multiple types of recommendations across the platform. First, we address the reason why implementing recommender systems can benefit online platforms and the state of the art in that area. Then, a proposal and implementation of a customised system are presented, and its results discussed. © 2020, Springer Nature Switzerland AG.},
note = {cited By 2; Conference of 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference Date: 4 November 2020 Through 6 November 2020; Conference Code:251049},
keywords = {Data mining, Electronic commerce; Engines; Online systems; Recommender systems; User experience, Human evolution; Mining engines; Online platforms; Personalisation; State of the art},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Costa2020322,
title = {Anticipating Maintenance in Telecom Installation Processes},
author = {D. Costa and C. Pereira and H. Peixoto and J. Machado},
editor = {Camacho D. Novais P. Analide C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097197295&doi=10.1007%2f978-3-030-62365-4_31&partnerID=40&md5=88bb363c6bd4e9c6c02cb8c755a193cc},
doi = {10.1007/978-3-030-62365-4_31},
issn = {03029743},
year  = {2020},
date = {2020-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {12490 LNCS},
pages = {322-334},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Improving customer experience is crucial in any industry, especially in telecommunications, where competition is a constant factor. Today, all telecommunications companies rely on the massive amount of data generated daily to get to know the customer or study their behavior and thus create new effective strategies for their business. Within the most varied user experiences, the process of installing new services can be an event that raises doubts about their operation, degrade the user experience, or, in extreme cases, lead to maintenance interventions. Therefore, the use of advanced predictive models that can predict such occurrences become vital. With this, the company can anticipate the cases that will be problematic and reduce the number of negative experiences. The main objective of this work is to create a predictive model that, through all the available data history, can predict which customers will contact the customer service with problems derived from the installation process and have a following maintenance intervention. After analyzing an unbalanced dataset with approximately 560K entries from a Portuguese telecommunications company, and resorting to the CRISP-DM methodology for modeling, the best results were found with LightGBM which obtained an AUPRC of 0.11 and AUROC of 0.62. The best trade-off between precision and recall was found with a threshold model of 0.43 in order to maximize recall while still avoiding a large number of false negatives. © 2020, Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference Date: 4 November 2020 Through 6 November 2020; Conference Code:251049},
keywords = {Competition; Economic and social effects; Installation; Maintenance; Predictive analytics; Sales; Telecommunication industry, Constant factors; Customer experience; Customer services; Negative experiences; Precision and recall; Predictive modeling; Predictive models; Threshold model, User experience},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lori2020309,
title = {Bridging the Gap of Neuroscience, Philosophy, and Evolutionary Biology to Propose an Approach to Machine Learning of Human-Like Ethics},
author = {N. Lori and D. Ferreira and V. Alves and J. Machado},
editor = {Camacho D. Novais P. Analide C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097195726&doi=10.1007%2f978-3-030-62365-4_30&partnerID=40&md5=453b756311f0c2c8bfb5432cb1eeb4a2},
doi = {10.1007/978-3-030-62365-4_30},
issn = {03029743},
year  = {2020},
date = {2020-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {12490 LNCS},
pages = {309-321},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The growing explosion of ideas such as Artificial Intelligence (AI), smart environments and ubiquitous computing has led to the creation of the Ambient Intelligence (AmI) paradigm. As AmI begins to take place, moving from a futuristic idea to a reality, we are gradually witnessing the creation of an omnipresent, responsive, and intelligent atmosphere in which thousands of tiny sensors and natural user interfaces will be embedded in our natural movements and in our social and physical interactions. Hence, a key challenge in this multi-disciplinary approach is to get machines to act according to ethical priorities that make sense to human beings. In this study, we improve the capacity for machine ethics to approach human ethics by assessing the computation of transaction values and we argue that it is possible to perform such a computation using recent work that describes the effects of human decision-making using an axiomatic framework. This paper clarifies the relationship between the brain’s 3-axes of neuroscience, the 3 Plato’s Transcendentals of philosophy and the biological evolution’s 3-components, as well as the top-down vs. bottom-up approaches to machine ethics. © 2020, Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference Date: 4 November 2020 Through 6 November 2020; Conference Code:251049},
keywords = {Ambient intelligence, Axiomatic framework; Biological evolution; Bottom up approach; Evolutionary biology; Human decision making; Multi-disciplinary approach; Natural user interfaces; Physical interactions, Biology; Decision making; Machine learning; Neurology; Philosophical aspects; User interfaces},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Cunha2020495,
title = {Improving Performance of Recommendation System Architecture},
author = {G. Cunha and H. Peixoto and J. Machado},
editor = {Camacho D. Novais P. Analide C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097189572&doi=10.1007%2f978-3-030-62365-4_47&partnerID=40&md5=0cda9a10356ac00b93d370305ff7d8ec},
doi = {10.1007/978-3-030-62365-4_47},
issn = {03029743},
year  = {2020},
date = {2020-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {12490 LNCS},
pages = {495-506},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The exponential appearance of online stores has implied higher market competitiveness and, consequently, companies need to adopt certain strategies to obtain greater prominence and gain clientele. This paper explores an architectural approach to incorporate a recommendation system in online stores, in order to offer a solution to achieve those goals. Developing the recommendation system infrastructure with NodeJS, based on a REST API, and according to microservices architecture concepts, has proven to be very efficient when it comes to managing great volumes of requests and data, and be capable to serve multiple tenants within a short response time. Clustering techniques were also implemented to increase the system’s performance and capability of handling requests. © 2020, Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference Date: 4 November 2020 Through 6 November 2020; Conference Code:251049},
keywords = {Architectural approach; Clustering techniques; Improving performance; Online store; Short response time, Architecture; Computer architecture; Electronic commerce, Recommender systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Coimbra2020368,
title = {Review of Trends in Automatic Human Activity Recognition in Vehicle Based in Synthetic Data},
author = {A. Coimbra and C. Neto and D. Ferreira and J. Duarte and D. Oliveira and F. Hak and F. Gonçalves and J. Fonseca and N. Lori and A. Abelha and J. Machado},
editor = {Camacho D. Novais P. Analide C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097146474&doi=10.1007%2f978-3-030-62365-4_35&partnerID=40&md5=595ad5027d15e25fb5fb60cbf36c6354},
doi = {10.1007/978-3-030-62365-4_35},
issn = {03029743},
year  = {2020},
date = {2020-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {12490 LNCS},
pages = {368-376},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Driverless vehicles are more and more becoming a reality. However, people still have some concerns in using them, the main concern is fear, hence the importance of creating a surveillance system inside those vehicles. For the detection and classification of human movements to be possible it is necessary to train the system with data representative enough for all kinds of possibilities. Although the production of large quantities of data becomes an expensive process and adds the problem of data protection, the use of synthetic data once they are artificially generated allows lower costs and eliminates the problem of data protection. A bibliographic study was carried out in this paper with articles from 2017 or later on the use of synthetic data. In these studies, it is noted that synthetic data is widely used with good results. As far as image capture is concerned, they show that 3D cameras have better results, but they are more expensive, so 2D cameras are more often used with later conversion to 3D images. The stitched puppet (SP) model is capable of adapting to the most difficult poses having obtained good results in its application in the FAUST dataset. © 2020, Springer Nature Switzerland AG.},
note = {cited By 1; Conference of 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference Date: 4 November 2020 Through 6 November 2020; Conference Code:251049},
keywords = {3-D cameras; Driverless; Human activity recognition; Human movements; Image captures; ITS applications; Surveillance systems; Synthetic data, Cameras; Privacy by design, Vehicles},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Jesus2020549,
title = {Review of Trends in Automatic Human Activity Recognition Using Synthetic Audio-Visual Data},
author = {T. Jesus and J. Duarte and D. Ferreira and D. Durães and F. Marcondes and F. Santos and M. Gomes and P. Novais and F. Gonçalves and J. Fonseca and N. Lori and A. Abelha and J. Machado},
editor = {Camacho D. Novais P. Analide C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097138094&doi=10.1007%2f978-3-030-62365-4_53&partnerID=40&md5=4f6f299f14229712acda0326b1718e1d},
doi = {10.1007/978-3-030-62365-4_53},
issn = {03029743},
year  = {2020},
date = {2020-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {12490 LNCS},
pages = {549-560},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {An in-depth study of knowledge and technologies was made related to the various scientific, technical, and industrial domains necessary for the acquisition of skills and capabilities for the design and development of a multisensory fusion system for vehicle cockpits. After an extensive literature review, it was possible to determine the baselines of the solution to be developed and obtain a pipeline prototype. © 2020, Springer Nature Switzerland AG.},
note = {cited By 4; Conference of 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference Date: 4 November 2020 Through 6 November 2020; Conference Code:251049},
keywords = {Artificial intelligence, Audio-visual data; Design and Development; Human activity recognition; In-depth study; Literature reviews; Multi-sensory fusion, Computer science; Computers},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Fernandes2020466,
title = {How to Assess the Acceptance of an Electronic Health Record System?},
author = {C. Fernandes and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Reis L. P. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085516709&doi=10.1007%2f978-3-030-45697-9_45&partnerID=40&md5=14be6b4c2a70e6c5d450ef8e4ea8bb3c},
doi = {10.1007/978-3-030-45697-9_45},
issn = {21945357},
year  = {2020},
date = {2020-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1161 AISC},
pages = {466-475},
publisher = {Springer},
abstract = {Being able to access a patient’s clinical data in due time is critical to any medical setting. Clinical data is very diverse both in content and in terms of which system produces it. The Electronic Health Record (EHR) aggregates a patient’s clinical data and makes it available across different systems. Considering that user’s resistance is a critical factor in system implementation failure, the understanding of user behavior remains a relevant object of investigation. The purpose of this paper is to outline how we can assess the technology acceptance of an EHR using the Technology Acceptance Model 3 (TAM3) and the Delphi methodology. An assessment model is proposed in which findings are based on the results of a questionnaire answered by health professionals whose activities are supported by the EHR technology. In the case study simulated in this paper, the results obtained showed an average of 3 points and modes of 4 and 5, which translates to a good level of acceptance. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 8th World Conference on Information Systems and Technologies, WorldCIST 2020 ; Conference Date: 7 April 2020 Through 10 April 2020; Conference Code:240259},
keywords = {Assessment models; Delphi methodology; Electronic health record; Electronic health record systems; Health professionals; System implementation; Technology acceptance; Technology acceptance model, Behavioral research; Information systems; Information use; Records management, eHealth},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neto2020212,
title = {Prediction of Length of Stay for Stroke Patients Using Artificial Neural Networks},
author = {C. Neto and M. Brito and H. Peixoto and V. Lopes and A. Abelha and J. Machado},
editor = {Reis L. P. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085480460&doi=10.1007%2f978-3-030-45688-7_22&partnerID=40&md5=b7fa77048d0c44a8862a54bdc721c2a8},
doi = {10.1007/978-3-030-45688-7_22},
issn = {21945357},
year  = {2020},
date = {2020-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1159 AISC},
pages = {212-221},
publisher = {Springer},
abstract = {Strokes are neurological events that affect a certain area of the brain. Since brain controls fundamental body activities, brain cell deterioration and dead can lead to serious disabilities and poor life quality. This makes strokes the leading cause of disabilities and mortality worldwide. Patients that suffer strokes are hospitalized in order to be submitted to surgery and receive recovery therapies. Thus, it’s important to predict the length of stay for these patients, since it can be costly to them and their family, as well as to the medical institutions. The aim of this study is to make a prediction on the number of days of patients’ hospital stays based on information available about the neurological event that happened, the patient’s health status and surgery details. A neural network was put to test with three attribute subsets with different sizes. The best result was obtained with the subset with fewer features obtaining a RMSE and a MAE of 5.9451 and 4.6354, respectively. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 8; Conference of 8th World Conference on Information Systems and Technologies, WorldCIST 2020 ; Conference Date: 7 April 2020 Through 10 April 2020; Conference Code:240259},
keywords = {Body activities; Brain controls; Different sizes; Health status; Length of stay; Life qualities; Medical institutions; Stroke patients, Brain; Deterioration; Forecasting; Information systems; Information use; Neural networks; Neurology; Surgery, Patient rehabilitation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sousa2020510,
title = {Step Towards Monitoring Intelligent Agents in Healthcare Information Systems},
author = {R. Sousa and D. Ferreira and A. Abelha and J. Machado},
editor = {Reis L. P. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085466894&doi=10.1007%2f978-3-030-45697-9_50&partnerID=40&md5=068709cd44fc36996b0993a90e8d8468},
doi = {10.1007/978-3-030-45697-9_50},
issn = {21945357},
year  = {2020},
date = {2020-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1161 AISC},
pages = {510-519},
publisher = {Springer},
abstract = {A platform for establishing interoperability between heterogeneous information systems implemented in a hospital environment is more a requirement than an option. The Agency for the Integration, Diffusion and Archiving of Medical and Clinical Information (AIDA) is an interoperability platform designed specifically to address the problem of integrating information from multiple systems and addressing interoperability, confidentiality, integrity and data availability. This article focuses on the relevance and need for such vigilance, finding and designing effective new ways to establish them. This study culminated in the creation of AIDAMonit, a surveillance platform developed and tested by ALGORITMI Center researchers, which has shown promise and is extremely beneficial for the well-functioning of the health facilities currently using the AIDA platform. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 4; Conference of 8th World Conference on Information Systems and Technologies, WorldCIST 2020 ; Conference Date: 7 April 2020 Through 10 April 2020; Conference Code:240259},
keywords = {Clinical information; Data availability; Health care information system; Health facilities; Heterogeneous information; Hospital environment; Integrating information; Surveillance platforms, Information use; Intelligent agents; Interoperability; Medical computing, Medical information systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Faria2020364,
title = {Intelligent support system for the provision of inpatient care},
author = {S. Faria and D. Oliveira and A. Abelha and J. Machado},
editor = {Montenegro Marin C. E. Ferras C. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080957489&doi=10.1007%2f978-3-030-40690-5_36&partnerID=40&md5=a95340e25e84258ad28a4bdcb78eac48},
doi = {10.1007/978-3-030-40690-5_36},
issn = {21945357},
year  = {2020},
date = {2020-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1137 AISC},
pages = {364-374},
publisher = {Springer},
abstract = {Inpatient care is seen as a rigorous healthcare environment, as several daily tasks are performed to provide adequate treatment to inpatients and a minor flaw in these tasks may result in irreversible damage to patients. It is therefore required that the information related to the patient is always updated and available to all health professionals. Thus, comes up the motivation of the project described in this paper, which presents an intelligent system to support the practice of inpatient healthcare through a Web platform that allows the monitoring of patients admitted to a health facility. Thus, the developed system culminates in an application where all relevant information is gathered to monitor the different hospitalization episodes, presenting this information in a simplistic and intuitive way and alerting the professionals to the occurrence of events related to medical exams and analysis, surgical procedures, among others. This paper presents the architecture, the requirements and a SWOT analysis of the solution proposed, the main conclusions and a proposed future work. © Springer Nature Switzerland AG 2020.},
note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2020 ; Conference Date: 5 February 2020 Through 7 February 2020; Conference Code:236869},
keywords = {Clinical decision support systems; Health facilities; Health professionals; Healthcare environments; Intelligent support; Irreversible damage; Support systems; Surgical procedures, Decision support systems; Health care; Intelligent agents; Intelligent systems; Multi agent systems; Patient treatment, Medical information systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2020104,
title = {The development of a business intelligence web application to support the decision-making process regarding absenteeism in the workplace},
author = {S. Oliveira and M. Esteves and R. Cernadas and A. Abelha and J. Machado},
editor = {Montenegro Marin C. E. Ferras C. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080919971&doi=10.1007%2f978-3-030-40690-5_11&partnerID=40&md5=ed9eefa756abc7615c2439c43b93b034},
doi = {10.1007/978-3-030-40690-5_11},
issn = {21945357},
year  = {2020},
date = {2020-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1137 AISC},
pages = {104-113},
publisher = {Springer},
abstract = {Nowadays, one of the biggest concerns of industries all over the world is situations regarding absenteeism, since it has a great impact on the productivity and economy of companies, as well as on the health of their employees. The major causes of absenteeism appear to be work accidents and sickness leaves, which lead to the attempt by companies of understanding how the workload is related to the health of their collaborators and, consequently, to absenteeism. Thus, this paper proposes the design and development of a Web Application based on Business Intelligence indicators in order to help the health and human resources professionals of a Portuguese company analyse the relation between absenteeism and the health and lifestyle of employees, with the intention of concluding whether the work executed on the company is harming workers’ health. Furthermore, it is intended to discover the principal motives for the numerous and more frequent absences in this company, so that it is possible to decrease the absenteeism rate and, hence, improve the decision-making process. This platform will also provide higher quality healthcare and the possibility to find patterns in the absence of collaborators, as well as reduce time-waste and errors. © Springer Nature Switzerland AG 2020.},
note = {cited By 1; Conference of International Conference on Information Technology and Systems, ICITS 2020 ; Conference Date: 5 February 2020 Through 7 February 2020; Conference Code:236869},
keywords = {Absenteeism; Decision making process; Design and Development; Information and Communication Technologies; Quality healthcare; Reduce time; WEB application; Work accidents, Competitive intelligence; Information analysis; Personnel; Telemedicine, Decision making},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Neto2019,
title = {Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients},
author = {C. Neto and M. Brito and V. Lopes and H. Peixoto and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079144928&doi=10.3390%2fe21121163&partnerID=40&md5=dcc28a65a249c1252bde76716a2b6f26},
doi = {10.3390/e21121163},
issn = {10994300},
year  = {2019},
date = {2019-01-01},
journal = {Entropy},
volume = {21},
number = {12},
publisher = {MDPI AG},
abstract = {The development of malign cells that can grow in any part of the stomach, known as gastric cancer, is one of the most common causes of death worldwide. In order to increase the survival rate in patients with this condition, it is essential to improve the decision-making process leading to a better and more efficient selection of treatment strategies. Nowadays, with the large amount of information present in hospital institutions, it is possible to use data mining algorithms to improve the healthcare delivery. Thus, this study, using the CRISP methodology, aims to predict not only the mortality associated with this disease, but also the occurrence of any complication following surgery. A set of classification models were tested and compared in order to improve the prediction accuracy. The study showed that, on one hand, the J48 algorithm using oversampling is the best technique to predict the mortality in gastric cancer patients, with an accuracy of approximately 74%. On the other hand, the rain forest algorithm using oversampling presents the best results when predicting the possible occurrence of complications among gastric cancer patients after their in-hospital stays, with an accuracy of approximately 83%. © 2019 The Author(s).},
note = {cited By 30},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Aqra2019,
title = {Incremental algorithm for association rule mining under dynamic threshold},
author = {I. Aqra and N. A. Ghani and C. Maple and J. Machado and N. S. Safa},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077333072&doi=10.3390%2fapp9245398&partnerID=40&md5=9500d6d6278832a24d28d3a79774173c},
doi = {10.3390/app9245398},
issn = {20763417},
year  = {2019},
date = {2019-01-01},
journal = {Applied Sciences (Switzerland)},
volume = {9},
number = {24},
publisher = {MDPI AG},
abstract = {Data mining is essentially applied to discover new knowledge from a database through an iterative process. The mining process may be time consuming for massive datasets. A widely used method related to knowledge discovery domain refers to association rule mining (ARM) approach, despite its shortcomings in mining large databases. As such, several approaches have been prescribed to unravel knowledge. Most of the proposed algorithms addressed data incremental issues, especially when a hefty amount of data are added to the database after the latest mining process. Three basic manipulation operations performed in a database include add, delete, and update. Any method devised in light of data incremental issues is bound to embed these three operations. The changing threshold is a long-standing problem within the data mining field. Since decision making refers to an active process, the threshold is indeed changeable. Accordingly, the present study proposes an algorithm that resolves the issue of rescanning a database that had been mined previously and allows retrieval of knowledge that satisfies several thresholds without the need to learn the process from scratch. The proposed approach displayed high accuracy in experimentation, as well as reduction in processing time by almost two-thirds of the original mining execution time. © 2019 by the authors.},
note = {cited By 26},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Esteves2019,
title = {A proof of concept of a mobile health application to support professionals in a portuguese nursing home},
author = {M. Esteves and M. Esteves and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072584446&doi=10.3390%2fs19183951&partnerID=40&md5=139237452f520e30761469345431e3d0},
doi = {10.3390/s19183951},
issn = {14248220},
year  = {2019},
date = {2019-01-01},
journal = {Sensors (Switzerland)},
volume = {19},
number = {18},
publisher = {MDPI AG},
abstract = {Over the past few years, the rapidly aging population has been posing several challenges to healthcare systems worldwide. Consequently, in Portugal, nursing homes have been getting a higher demand, and health professionals working in these facilities are overloaded with work. Moreover, the lack of health information and communication technology (HICT) and the use of unsophisticated methods, such as paper, in nursing homes to clinically manage residents lead to more errors and are time-consuming. Thus, this article proposes a proof of concept of a mobile health (mHealth) application developed for the health professionals working in a Portuguese nursing home to support them at the point-of-care, namely to manage and have access to information and to help them schedule, perform, and digitally record their tasks. Additionally, clinical and performance business intelligence (BI) indicators to assist the decision-making process are also defined. Thereby, this solution aims to introduce technological improvements into the facility to improve healthcare delivery and, by taking advantage of the benefits provided by these improvements, lessen some of the workload experienced by health professionals, reduce time-waste and errors, and, ultimately, enhance elders’ quality of life and improve the quality of the services provided. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.},
note = {cited By 6},
keywords = {Competitive intelligence; Decision making; Hospitals; Information analysis; mHealth; Nursing, Databases, Decision making process; Elders; Health informations; Health professionals; Mobile Health (M-Health); Mobile health application; Nursing homes; Technological improvements, factual database; health care delivery; health care personnel; human; injury; medical information; medical record; mobile application; nursing home; organization and management; point of care system; Portugal; professional standard; proof of concept, Factual; Delivery of Health Care; Health Communication; Health Personnel; Humans; Medical Records; Mobile Applications; Nursing Homes; Point-of-Care Systems; Portugal; Professional Role; Proof of Concept Study; Wounds and Injuries, Home health care},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Ribeiro2019,
title = {A Comparative Study of Optical Character Recognition in Health Information System},
author = {M. R. M. Ribeiro and D. Julio and V. Abelha and A. Abelha and J. MacHado},
editor = {Quintal F. Morgado-Dias F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075045115&doi=10.1109%2fCEAP.2019.8883448&partnerID=40&md5=1de6f86565f23ef15d48173be053cf26},
doi = {10.1109/CEAP.2019.8883448},
isbn = {9781728129624},
year  = {2019},
date = {2019-01-01},
journal = {2019 International Conference on Engineering Applications, ICEA 2019 - Proceedings},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Most Health Institutes are transitioning between documents in physical format and digital format. It is pertinent and important to develop applications that helps health professionals on this transition. An application that would aid the process of digitalization of documents was developed using a Python library. To help with the decision of which library to use, a study was made regarding the precision and speed of execution of PyOCR, PyTesseract and TesseOCR. © 2019 IEEE.},
note = {cited By 6; Conference of 2019 International Conference on Engineering Applications, ICEA 2019 ; Conference Date: 8 July 2019 Through 11 July 2019; Conference Code:153483},
keywords = {Comparative studies; Digital format; Health information systems; Health professionals; Python; Wrapper, Health, High level languages; Optical character recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miranda2019,
title = {Intelligent Nutrition in Healthcare and Continuous Care},
author = {R. Miranda and D. Ferreira and A. Abelha and J. MacHado},
editor = {Quintal F. Morgado-Dias F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075038092&doi=10.1109%2fCEAP.2019.8883496&partnerID=40&md5=c6bdb1d242e0971f21f15cf024de6e83},
doi = {10.1109/CEAP.2019.8883496},
isbn = {9781728129624},
year  = {2019},
date = {2019-01-01},
journal = {2019 International Conference on Engineering Applications, ICEA 2019 - Proceedings},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In the healthcare industry, the patient's nutrition is a key factor in their treatment process. Every user has their own specific nutritional needs and requirements. An appropriate nutrition policy can therefore help the patient's recovery process and alleviate possible symptoms. Food recommender systems are platforms that offer personalised suggestions of recipes to users. However, there is a lack of usage of recipe recommendation systems in the healthcare sector. Multiple challenges in representing the domain of food and the patient's needs make it complicated to implement these systems. The present project aims to develop a platform for an intelligent planning of the user's meals, based on their clinical conditions. The application of machine learning algorithms on nutrition, in healthcare services and continuous care is thus a key topic of research. This platform will be tested and deployed at the Social Cafeteria of Vila Verde (Cantina Social da Santa Casa da Misericórdia de Vila Verde). The development of this project will use the Design Science Research (DSR) investigation methodology, ensuring that the solution to the problem accomplishes all needs and requirements of the professionals, while elucidating new knowledge both for the institution and the scientific community. © 2019 IEEE.},
note = {cited By 7; Conference of 2019 International Conference on Engineering Applications, ICEA 2019 ; Conference Date: 8 July 2019 Through 11 July 2019; Conference Code:153483},
keywords = {Clinical conditions; Design science researches (DSR); Healthcare industry; Healthcare sectors; Healthcare services; Intelligent planning; Scientific community; Treatment process, Decision support systems; Learning algorithms; Learning systems; Machine learning; Patient rehabilitation; Patient treatment; Recommender systems, Nutrition},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{DeBrito2019,
title = {Prediction of mortality and occurrence of complications for gastric cancer patients},
author = {M. A. De Brito and C. Neto and A. Abelha and J. MacHado},
editor = {Quintal F. Morgado-Dias F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075034483&doi=10.1109%2fCEAP.2019.8883494&partnerID=40&md5=47c8da4f5068b2f915c5edce9859a4dc},
doi = {10.1109/CEAP.2019.8883494},
isbn = {9781728129624},
year  = {2019},
date = {2019-01-01},
journal = {2019 International Conference on Engineering Applications, ICEA 2019 - Proceedings},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Gastric cancer is one of the most prevalent types of cancer in the whole world, affecting millions of people over the last decades. Its symptoms are ambiguous, which leads to late diagnoses, reducing the patients' chances of survival. In most countries, routine screenings are not usual, which also contributes to the detection of this gastric malignancy in later and more dangerous (and often fatal)stages. One of the main focus of improving healthcare services related to gastric cancer relies on increasing the survival rates. This and predicting if a patient will suffer from any complication following the surgery can aid the healthcare professionals in selecting better and more efficient treatment strategies. Thus, this constitutes as the aims of this study which will test and compare a set of classification models in order to improve the prediction accuracy. Data mining techniques will be put into use, since it's been proved they are one of the best ways of producing useful information for many businesses, including healthcare. © 2019 IEEE.},
note = {cited By 1; Conference of 2019 International Conference on Engineering Applications, ICEA 2019 ; Conference Date: 8 July 2019 Through 11 July 2019; Conference Code:153483},
keywords = {Classification (of information); Diagnosis; Diseases; Forecasting; Health care; Patient treatment, Classification models; complication occurrence; Efficient treatment; Gastric cancers; Health care professionals; Healthcare services; Mortality rate; Prediction accuracy, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Fonseca2019219,
title = {Smart mobile computing in pregnancy care},
author = {F. Fonseca and H. Peixoto and J. Braga and J. Machado and A. Abelha},
editor = {Jin Y. Lee G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077605330&partnerID=40&md5=c00965c33272a816719482518f898e6a},
isbn = {9781510885967},
year  = {2019},
date = {2019-01-01},
journal = {Proceedings of 34th International Conference on Computers and Their Applications, CATA 2019},
pages = {219-224},
publisher = {The International Society for Computers and Their Applications (ISCA)},
abstract = {Pregnancy is a period of changes. With all the information available and all the questions raised, it may also be an overwhelming period. Mobiles phones might be a solution for pregnant women to follow their pregnancy through Electronic Maternity Records (EMR). Therefore, this paper aims to propose an EMR to help women during their pregnancy. Firstly, the importance of Personal Health Records (PHRs) as well as mHealth is overviewed. Secondly, the types of mobile apps are presented with their pros and cons and the concept of Progressive Web App (PWA) is introduced. In order to understand the features that pregnancy mobile apps are now offering and the ones they are missing, eight apps are analysed. Lastly, the features and architecture of the proposed EMR are described and discussed. Since PWAs are a recent technology and a promising alternative to the three classic types of mobile development, it is also the technology used to develop the proposed EMR. Copyright © 2012-2020 easychair.org. All rights reserved.},
note = {cited By 5; Conference of 34th International Conference on Computers and Their Applications, CATA 2019 ; Conference Date: 18 March 2019 Through 20 March 2019; Conference Code:155934},
keywords = {Health care, Mobile apps; Personal health record; Pregnant woman; Web App, Obstetrics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Esteves2019338,
title = {A mobile health application to assist health professionals: A case study in a Portuguese nursing home},
author = {M. Esteves and M. Esteves and A. Abelha and J. Machado},
editor = {Maciaszek L. Maciaszek L. Ziefle M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067444638&doi=10.5220%2f0007809203380345&partnerID=40&md5=4d354c1a3d1357c1871364625e73ea61},
doi = {10.5220/0007809203380345},
isbn = {9789897583681},
year  = {2019},
date = {2019-01-01},
journal = {ICT4AWE 2019 - Proceedings of the 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health},
pages = {338-345},
publisher = {SciTePress},
abstract = {The rapidly aging population has been a matter of concern over years since this problematic has been posing several challenges to healthcare systems worldwide. In Portugal, which is one of the countries with the largest aging population, nursing homes have been getting a higher demand, and health professionals are overloaded with work. Furthermore, the fact that few nursing homes use health information and communication technology (ICT) resorting to paper to record information and clinically manage their residents is a tremendous problem, since this method is more prone to errors and time-consuming. Thus, this paper proposes the design and development of a mobile application for health professionals working in a Portuguese nursing home with the intention of assisting them at the point-of-care, by recording and providing all the necessary information, and helping them to schedule, perform, and digitally record their tasks. This solution will help health professionals to provide better care, by reducing time-waste and errors, and, consequently, to improve elders' quality of life. A mobile solution was chosen since a hand-held device, which can be used anywhere and anytime, is able to give access and store all the needed information at the point-of-care. Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved},
note = {cited By 3; Conference of 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2019 ; Conference Date: 2 May 2019 Through 4 May 2019; Conference Code:148435},
keywords = {Elders; Ethical issues; Health informations; Health professionals; Nursing homes, Hand held computers; Hospitals; mHealth; Nursing, Home health care},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Cruz2019557,
title = {Application of data mining for the prediction of prophylactic measures in patients at risk of deep vein thrombosis},
author = {M. Cruz and M. Esteves and H. Peixoto and A. Abelha and J. Machado},
editor = {Reis L. P. Costanzo S. Adeli H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065104150&doi=10.1007%2f978-3-030-16187-3_54&partnerID=40&md5=d2faa227145ef872f4f15e8948021077},
doi = {10.1007/978-3-030-16187-3_54},
issn = {21945357},
year  = {2019},
date = {2019-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {932},
pages = {557-567},
publisher = {Springer Verlag},
abstract = {In the last decades, with the increase in the amount of data stored in the healthcare industry, it is also extended the possibility of obtaining important information to support the decision-making process of health professionals. This article has as evidence to apply Data Mining (DM) techniques to health databases of patients with medical Deep Vein Thrombosis (DVT) risk, with the objective of classifying, based on different attributes obtained in medical discharge reports, the main prophylactic measures taken. Therefore, to achieve this goal, the free software Weka was used aiming to facilitate the process of DM, along with the algorithms chosen. In view of this, it was concluded that the service to which each patient is associated is the most relevant factor for prophylactic measures followed by the age range to which the patient belongs. This study also deduces that it can be possible to obtain classifiers capable of predicting the best prophylactic measures with a qualitative level similar as one of a health professional and, thereafter, it can be possible to obtain the classification. © Springer Nature Switzerland AG 2019.},
note = {cited By 2; Conference of World Conference on Information Systems and Technologies, WorldCIST 2019 ; Conference Date: 16 April 2019 Through 19 April 2019; Conference Code:224789},
keywords = {Blood vessels; Classification (of information); Decision making; Diseases; Forecasting; Health risks; Information systems; Information use; Medical computing; Risk assessment, Data mining, Decision making process; Deep vein thrombosis; Free software; Health professionals; Healthcare industry; Prophylactic measures; Weka},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ferreira2019527,
title = {Steps towards online monitoring systems and interoperability},
author = {D. Ferreira and C. Neto and J. Machado and A. Abelha},
editor = {Reis L. P. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065103983&doi=10.1007%2f978-3-030-16187-3_51&partnerID=40&md5=854638cdcd64103f4557f6c7543f3351},
doi = {10.1007/978-3-030-16187-3_51},
issn = {21945357},
year  = {2019},
date = {2019-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {932},
pages = {527-536},
publisher = {Springer Verlag},
abstract = {In the health area, there is, on a daily basis, an enormous amount of data being produced and disseminated. The fast-growing amount of collected data and the rich knowledge, possibly life-saving, that could be extracted from these data has demanded the search of new ways to ensure the reliability and availability of the information with an emphasis on the efficient use of information technology tools. Although the main focus of the information systems is the health professionals who contact directly with the patient, it is also imperative to have tools for the background of the health units (information services, managers of systems, etc.). The main purpose of this work is the development of an innovative and interactive web platform for the daily monitoring of the web services activities of a Portuguese hospital, Centro Hospitalar do Porto (CHP). This platform is a web application developed in React that aims to ensure the correct functioning of the web services, that are responsible for numerous tasks within the hospital environment, and which failure could result in disastrous consequences, both for the health institution and for the patients. The development of the web application followed the six stages of the Design Science Research (DSR) methodology and was submitted to the Strengths Weaknesses Opportunities and Threats (SWOT) analysis, which results were considered optimistic. © Springer Nature Switzerland AG 2019.},
note = {cited By 0; Conference of World Conference on Information Systems and Technologies, WorldCIST 2019 ; Conference Date: 16 April 2019 Through 19 April 2019; Conference Code:224789},
keywords = {Design science researches (DSR); Health professionals; Hospital environment; Information technology tools; On-line monitoring system; Opportunities and threat (SWOT); Reliability and availability; WEB application, Health; Hospitals; Information services; Information systems; Information technology; Information use; Medical computing; Monitoring; Online systems; Web services; Websites, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Loreto2019568,
title = {Predicting low birth weight babies through data mining},
author = {P. Loreto and H. Peixoto and A. Abelha and J. Machado},
editor = {Adeli H. Rocha A. Costanzo S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065068373&doi=10.1007%2f978-3-030-16187-3_55&partnerID=40&md5=a44b920a5b272005f074e6603d35e795},
doi = {10.1007/978-3-030-16187-3_55},
issn = {21945357},
year  = {2019},
date = {2019-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {932},
pages = {568-577},
publisher = {Springer Verlag},
abstract = {Low Birth Weight (LBW) babies have a high risk of developing certain health conditions throughout their lives that affect negatively their quality of life. Therefore, a Decision Support System (DSS) that predicts whether a baby will be born with LBW would be of great interest. In this study, six different Data Mining (DM) algorithms are tested for five different scenarios. The scenarios combine information about the mother’s physical characteristics and habits, and the gestation. Results are promising and the best model achieved a sensitivity of 91,4% and a specificity of 99%. Good results were also achieved without considering the gestational age, which showed that the use of DM might be a good alternative to the traditional medical imaging exams in the prediction of LBW early in the pregnancy. © Springer Nature Switzerland AG 2019.},
note = {cited By 11; Conference of World Conference on Information Systems and Technologies, WorldCIST 2019 ; Conference Date: 16 April 2019 Through 19 April 2019; Conference Code:224789},
keywords = {Artificial intelligence; Classification (of information); Decision support systems; Health risks; Information systems; Information use; Medical imaging, CRISP-DM; Decision support system (dss); Gestational age; Health condition; Knowledge discovery in database; Low birth weights; Physical characteristics; Quality of life, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neto2019537,
title = {Improving healthcare delivery with new interactive visualization methods},
author = {C. Neto and D. Ferreira and A. Abelha and J. Machado},
editor = {Adeli H. Costanzo S. Reis L.P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065059735&doi=10.1007%2f978-3-030-16187-3_52&partnerID=40&md5=c29515a1ef6bed1adaedb3a89b90a93f},
doi = {10.1007/978-3-030-16187-3_52},
issn = {21945357},
year  = {2019},
date = {2019-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {932},
pages = {537-546},
publisher = {Springer Verlag},
abstract = {Over the last years, the implementation and evolution of computer resources in hospital institutions has been improving both the financial and temporal efficiency of clinical processes, as well as the security in the transmission and maintenance of their data, also ensuring the reduction of clinical risk. Diagnosis, treatment and prevention of human illness are some of the most information-intensive of all intellectual tasks. Health providers often do not have or cannot find the information they need to respond quickly and appropriately to patient’s medical problems. Failure to review and follow up on patient’s test results in a timely manner, for example, represents a patient’s safety and malpractice concern. Therefore, it was sought to identify problems in a medical exams results management system and possible ways to improve this system in order to reduce both clinical risks and hospital costs. In this sense, a new medical exams visualization platform (AIDA-MCDT) was developed, specifically in the Hospital Center of Porto (CHP), with several new functionalities in order to make this process faster, intuitive and efficient, always guaranteeing the confidentiality and protection of patients’ personal data and significantly improving the usability of the system, leading to a better health care delivery. © Springer Nature Switzerland AG 2019.},
note = {cited By 1; Conference of World Conference on Information Systems and Technologies, WorldCIST 2019 ; Conference Date: 16 April 2019 Through 19 April 2019; Conference Code:224789},
keywords = {Computer resources; Electronic health record; Health information systems; Healthcare delivery; Interactive visualizations; Management systems; Medical exams; Visualization platforms, Data visualization; Diagnosis; Health care; Hospitals; Information technology; Information use; Management information systems; Medical problems; Visualization, Medical information systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ferreira2019611,
title = {Adaptive business intelligence in healthcare - A platform for optimising surgeries},
author = {J. Ferreira and F. Portela and J. Machado and M. F. Santos},
editor = {Reis L. P. Rocha A. Costanzo S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065057373&doi=10.1007%2f978-3-030-16187-3_59&partnerID=40&md5=6d729b036caa5cbfd9e882dcecf95978},
doi = {10.1007/978-3-030-16187-3_59},
issn = {21945357},
year  = {2019},
date = {2019-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {932},
pages = {611-620},
publisher = {Springer Verlag},
abstract = {Adaptive Business Intelligence (ABI) combines predictive with prospective analytics in order to give support to the decision making process. Surgery scheduling in hospital operating rooms is a high complex task due to huge volume of surgeries and the variety of combinations and constraints. This type of activity is critical and is often associated to constant delays and significant rescheduling. The main task of this work is to provide an ABI based platform capable of estimating the time of the surgeries and then optimising the scheduling (minimizing the waste of resources). Combining operational data with analytical tools this platform is able to present complex and competitive information to streamline surgery scheduling. A case study was explored using data from a portuguese hospital. The best achieved relative absolute error attained was 6.22%. The paper also shows that the approach can be used in more general applications. © Springer Nature Switzerland AG 2019.},
note = {cited By 2; Conference of World Conference on Information Systems and Technologies, WorldCIST 2019 ; Conference Date: 16 April 2019 Through 19 April 2019; Conference Code:224789},
keywords = {Absolute error; Analytical tool; Complex task; Constant delays; Decision making process; General applications; Operational data; Waste of resources, Artificial intelligence; Decision making; Decision support systems; Hospitals; Information systems; Information use; Predictive analytics; Scheduling, Surgery},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Peixoto201937,
title = {Predicting Postoperative Complications for Gastric Cancer Patients Using Data Mining},
author = {H. Peixoto and A. Francisco and A. Duarte and M. Esteves and S. Oliveira and V. Lopes and A. Abelha and J. Machado},
editor = {Branco P. Portela C.F. Magalhaes L.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065038722&doi=10.1007%2f978-3-030-16447-8_4&partnerID=40&md5=86caef7589a1d51de058bdb33623a155},
doi = {10.1007/978-3-030-16447-8_4},
issn = {18678211},
year  = {2019},
date = {2019-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {273},
pages = {37-46},
publisher = {Springer Verlag},
abstract = {Gastric cancer refers to the development of malign cells that can grow in any part of the stomach. With the vast amount of data being collected daily in healthcare environments, it is possible to develop new algorithms which can support the decision-making processes in gastric cancer patients treatment. This paper aims to predict, using the CRISP-DM methodology, the outcome from the hospitalization of gastric cancer patients who have undergone surgery, as well as the occurrence of postoperative complications during surgery. The study showed that, on one hand, the RF and NB algorithms are the best in the detection of an outcome of hospitalization, taking into account patients’ clinical data. On the other hand, the algorithms J48, RF, and NB offer better results in predicting postoperative complications. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019.},
note = {cited By 2; Conference of 10th International Conference on Intelligent Technologies for Interactive Entertainment, INTETAIN 2018 ; Conference Date: 21 November 2018 Through 23 November 2018; Conference Code:225079},
keywords = {Artificial intelligence; Decision making; Decision support systems; Diseases; Forecasting; Hospitals; Surgery, Clinical data; Clinical decision support systems; CRISP-DM; Decision making process; Gastric cancers; Healthcare environments; Postoperative complications; WEKA, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Peixoto2019558,
title = {Predicting Death and Morbidity in Perforated Peptic Ulcer},
author = {H. Peixoto and L. Correia e Silva and S. Pereira and T. Jesus and V. Lopes and A. Abelha and J. Machado},
editor = {Rocha A. Ferras C. Paredes M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061376631&doi=10.1007%2f978-3-030-11890-7_54&partnerID=40&md5=bcd18b79f24170a953648e6219eeee3f},
doi = {10.1007/978-3-030-11890-7_54},
issn = {21945357},
year  = {2019},
date = {2019-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {918},
pages = {558-568},
publisher = {Springer Verlag},
abstract = {Peptic ulcers are defined as defects in the gastrointestinal mucosa that extend through the muscularis mucosae. Although not being the most common complication, perforations stand out as being the complication with the highest mortality rate. To predict the probability of mortality, several scoring systems based on clinical and biochemical parameters, such as the Boey and PULP scoring system have been developed. This article explores, using data mining in the medical data available, how the scoring systems perform when trying to predict mortality and patients’ state complication. We also try to conclude, from the two scoring systems presented, which predicts better the situations described. Regarding the results, we concluded that the PULP scoring allows a better mortality prediction achieving, in this case, above 90% accuracy, however, the results may be inconclusive due to the lack of patients who died in the dataset used. Regarding the complications, we concluded that, on the other hand, the Boey system achieves better results leading to a better prediction when it comes to predicting patients’ state complication. © 2019, Springer Nature Switzerland AG.},
note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2019 ; Conference Date: 6 February 2019 Through 8 February 2019; Conference Code:223499},
keywords = {Boey; Dataset; Death; Health complications; Scoring systems, Data mining, Diseases; Forecasting; Large dataset; Medical computing; Pulp},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Machado2019569,
title = {Predicting the Length of Hospital Stay After Surgery for Perforated Peptic Ulcer},
author = {J. Machado and A. C. Cardoso and I. Gomes and I. Silva and V. Lopes and H. Peixoto and A. Abelha},
editor = {Rocha A. Ferras C. Paredes M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061357086&doi=10.1007%2f978-3-030-11890-7_55&partnerID=40&md5=c2f374f26c35d638295694daa029c557},
doi = {10.1007/978-3-030-11890-7_55},
issn = {21945357},
year  = {2019},
date = {2019-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {918},
pages = {569-579},
publisher = {Springer Verlag},
abstract = {The management of peptic ulcer disease usually implies an urgent surgical procedure with the need of a patient’s hospital admission. By predicting the length of hospital stay of patients, improvements can be made regarding the quality of services provided to patients. This paper focuses on using real data to identify patterns in patients’ profiles and surgical events, in order to predict if patients will need hospital care for a shorter or longer period of time. This goal is pursued using a Data Mining process which follows the CRISP-DM methodology. In particular, classification models are built by combining different scenarios, algorithms and sampling methods. The data mining model which performed best achieved an accuracy of 87.30%, a specificity of 89.40%, and a sensitivity of 81.30%, using JRip, a rule-based algorithm and Cross Validation as a sampling method. © 2019, Springer Nature Switzerland AG.},
note = {cited By 1; Conference of International Conference on Information Technology and Systems, ICITS 2019 ; Conference Date: 6 February 2019 Through 8 February 2019; Conference Code:223499},
keywords = {Artificial intelligence; Classification (of information); Decision support systems; Diseases; Forecasting; Hospitals; Surgery, Classification models; CRISP-DM; Data mining process; Length of hospital stays; Peptic ulcer disease; Peptic ulcers; Rule based algorithms; Surgical procedures, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ferreira2018137,
title = {Predictive data mining in nutrition therapy},
author = {D. Ferreira and H. Peixoto and J. Machado and A. Abelha},
editor = {Gil P. Henriques J. Teixeira C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057334712&doi=10.1109%2fCONTROLO.2018.8516413&partnerID=40&md5=dee7234bc455a021e666a52be4dbb1fe},
doi = {10.1109/CONTROLO.2018.8516413},
isbn = {9781538653463},
year  = {2018},
date = {2018-01-01},
journal = {13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Proceedings},
pages = {137-142},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The assessment and measurement of health status in communities throughput the world is a massive information technology challenge. Data mining, plays a vital role in health care industry since it really has the potential to generate a knowledge-rich environment that reduces medical errors, decreases costs by increasing efficiency, improves the quality of clinical decisions and significantly enhances patient's outcomes and quality of life. This study falls within the context of nutrition evaluation and its main goal is to apply classification algorithms in order to predict if a patient needs to be followed by a nutrition specialist. One of the tools resorted in this study was the Waikato Environment for Knowledge Analysis (Weka in advance) Workbench since it allows to quickly try out and compare different machine learning solutions. The tasks involved in the development of this project included data preparation, data preprocessing, data transformation and cleaning, application of several classifiers and its respective evaluation through performance measures that include the confusion matrix, accuracy, error rate, and others. The accomplished results showed to be quite optimistic presenting promising values of performance measures. specifically an accuracy around 91 %. © 2018 IEEE.},
note = {cited By 9; Conference of 13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 ; Conference Date: 4 June 2018 Through 6 June 2018; Conference Code:141782},
keywords = {Artificial intelligence; Classification (of information); Health care; Information technology; Learning systems; Linear transformations; Medical computing; Metadata; Nutrition; Soft computing, Classification algorithm; Clinical decision; Confusion matrices; Data transformation; Healthcare industry; Knowledge analysis; Performance measure; Predictive data mining, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Neves20181123,
title = {A Deep-Big Data Approach to Health Care in the AI Age},
author = {J. Neves and H. Vicente and M. Esteves and F. Ferraz and A. Abelha and J. Machado and J. Machado and J. Neves and J. Ribeiro and L. Sampaio},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049118338&doi=10.1007%2fs11036-018-1071-6&partnerID=40&md5=29e3778023e191c94a2f9373999bab44},
doi = {10.1007/s11036-018-1071-6},
issn = {1383469X},
year  = {2018},
date = {2018-01-01},
journal = {Mobile Networks and Applications},
volume = {23},
number = {4},
pages = {1123-1128},
publisher = {Springer New York LLC},
abstract = {The intersection of these two trends is what we call The Issue and it is helping businesses in every industry to become more efficient and productive. One’s aim is to have an insight into the development and maintenance of comprehensive and integrated health information systems that enable sound policy and effective health system management in order to improve health and health care. Undeniably, different sorts of technologies have been developed, each with their own advantages and disadvantages, which will be sorted out by attending at the impact that Artificial Intelligence and Decision Support Systems have to everyone in the healthcare sector engaged to quality-of-care, i.e., making sure that doctors, nurses, and staff have the training and tools they need to do their jobs. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.},
note = {cited By 24},
keywords = {Artificial intelligence; Decision support systems; Deep learning; Health care; Information management; Knowledge representation; Logic programming; Medical computing; Medical imaging; Neural networks; Personnel training, Big data, Health systems; Healthcare sectors; Integrated health information systems; Knowledge representation and reasoning; Quality of care},
pubstate = {published},
tppubtype = {article}
}
@inbook{Neto20181580,
title = {Applied business intelligence in surgery waiting lists management},
author = {C. Neto and I. Dias and M. Santos and H. Peixoto and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059720501&doi=10.4018%2f978-1-5225-6915-2.ch072&partnerID=40&md5=80aa952e37b338e651f262132a3fef94},
doi = {10.4018/978-1-5225-6915-2.ch072},
isbn = {9781522569169; 9781522569152},
year  = {2018},
date = {2018-01-01},
journal = {Healthcare Policy and Reform: Concepts, Methodologies, Tools, and Applications},
volume = {3},
pages = {1580-1594},
publisher = {IGI Global},
abstract = {With the advent of computer science in hospitals, Electronic Health Record comes up, with the aim of bringing the new information technologies to the hospital environment with the promise not only to replace the paper process, but also to improve and provide better patient care. The operationalization of the EHR in supporting evidence-based practice, complex and conscientious decision-making, and improving the quality of healthcare delivery has been supported by the Business Intelligence (BI) technology. Since the beginning of the 1990s, the Portuguese health system has been confronted with a chronic problem, waiting time for surgery, due to inability to respond to demand for surgical therapy. Therefore, using business intelligence and information, obtained with the construction of dashboards, can help, for example, allocating hospital resources and reducing waiting times. © 2019, IGI Global.},
note = {cited By 4},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Reis2018459,
title = {Business intelligence for nutrition therapy},
author = {R. Reis and A. Mendonça and D. L. A. Ferreira and H. Peixoto and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059710567&doi=10.4018%2f978-1-5225-6915-2.ch022&partnerID=40&md5=eb6574122511d72cee5a403579a645c2},
doi = {10.4018/978-1-5225-6915-2.ch022},
isbn = {9781522569169; 9781522569152},
year  = {2018},
date = {2018-01-01},
journal = {Healthcare Policy and Reform: Concepts, Methodologies, Tools, and Applications},
volume = {1},
pages = {459-474},
publisher = {IGI Global},
abstract = {The assessment of health status in communities throughout the world is a massive information technology challenge. Data warehousing provides a flexible environment to support the business management and serve as an integrated repository for data. With the addition of models and analytic tools that have the potential to provide actionable information resources and support effective problem identification, critical decision-making, and strategy formulation, implementation, and evaluation. Of particular interest are the factors of influence like the patient’s height or weight and its impact on processes and results. A multidimensional process is a way to discover health care processes according to certain factors of influence. This study aims to implement a data warehousing environment for decision support, in the context of nutrition evaluation, to integrate data obtained from a health care facility. This paper highlights the implementation of Business Intelligence in health care settings allows searching and interpreting stored information to support decisions concerning people’s life. © 2019, IGI Global.},
note = {cited By 2},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Loreto2018525,
title = {Step towards progressive web development in obstetrics},
author = {P. Loreto and J. Braga and H. Peixoto and J. Machado and A. Abelha},
editor = {Yasar A. Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058285822&doi=10.1016%2fj.procs.2018.10.131&partnerID=40&md5=06c665e37ecdeab7953f0f7fe02842e3},
doi = {10.1016/j.procs.2018.10.131},
issn = {18770509},
year  = {2018},
date = {2018-01-01},
journal = {Procedia Computer Science},
volume = {141},
pages = {525-530},
publisher = {Elsevier B.V.},
abstract = {The aim of this paper is to develop a Personal Health Record (PHR) for the support of pregnant women. With this goal in mind, concepts such as PHR and their importance in the obstetrics field are overviewed, as well as mobile development strategies. The system was developed with the support of a medical institution and taking into account what pregnant women find useful. The developed app is a Progressive Web App (PWA). This is a recent technology that allows the same app to work on most devices, gives a native feel to it when using on mobile devices and enables offline support. Further testing is necessary to understand the impact that this system may have in the engagement of pregnant women and in birth outcomes. © 2018 The Authors. Published by Elsevier Ltd.},
note = {cited By 2; Conference of 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2018 ; Conference Date: 5 November 2018 Through 8 November 2018; Conference Code:142251},
keywords = {Development strategies; Medical institutions; Mobile Development; Personal health record; Pregnant woman; Web App; Web apps; Web development, mHealth, Obstetrics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Castro2018389,
title = {Employment of industrial wastes as agents for inclusion modification in molten steels},
author = {F. A. Castro and J. Santos and P. Lacerda and R. Pacheco and T. Teixeira and A. Silva and E. Soares and J. Machado and M. Abreu},
editor = {de Lurdes Lopes M. Castro F. Vilarinho C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058143986&partnerID=40&md5=2373e8e5056d3d50ab4a08f53fe3fd5d},
isbn = {9781138196698},
year  = {2018},
date = {2018-01-01},
journal = {WASTES - Solutions, Treatments and Opportunities II - Selected papers from the 4th edition of the International Conference Wastes: Solutions, Treatments and Opportunities, 2017},
pages = {389-394},
publisher = {CRC Press/Balkema},
abstract = {Chemical modification of sulphide and oxide inclusions is a normal practice to obtain more desirable mechanical properties in steels. For example, cryogenic resilience is a relevant property that may be affected relevantly by chemical and morphological modification of inclusions during liquid steel elaboration. This change in properties is done, in steelmaking, mostly during ladle treatment. In steel foundry practice, similar procedures are possible. In this work, it has been studied the employment of industrial residues, like calcium rich ones, for the purpose of treatment of molten steels. Results are evaluated and allowed to conclude for the usefulness of some of the wastes, enhancing better mechanical properties, especially at low temperatures. © 2018 Taylor & Francis Group, London, UK.},
note = {cited By 0; Conference of 4th International Conference Wastes: Solutions, Treatments and Opportunities, WASTES 2017 ; Conference Date: 25 September 2017 Through 26 September 2017; Conference Code:220569},
keywords = {Chemical modification; Mechanical properties; Steel foundry practice; Sulfur compounds, Industrial residues; Ladle treatment; Liquid steels; Low temperatures; Molten steel; Morphological modification; Oxide inclusion, Industrial wastes},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neves2018124,
title = {Waiting time screening in healthcare},
author = {J. Neves and H. Vicente and M. Esteves and F. Ferraz and A. Abelha and J. Machado and J. Machado and J. Neves},
editor = {Jung J. J. Kim P. Choi K.N.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057187471&doi=10.1007%2f978-3-319-98752-1_14&partnerID=40&md5=ff8c5917c0a23f21c5d0c79c6e4173b3},
doi = {10.1007/978-3-319-98752-1_14},
issn = {18678211},
year  = {2018},
date = {2018-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {248},
pages = {124-131},
publisher = {Springer Verlag},
abstract = {In Medical Imaging (MI), various technologies can be used to monitor the human body for diagnosing, monitoring or treating disease. Each type of technology provides different information about the body area that is being investigated or treated for a possible illness, injury or effectiveness of a medical treatment. Routine screening has identified malfunction detection in many otherwise asymptomatic patient images such as computed tomography or magnetic resonance. Studies have shown that, compared to patients whose disease was symptomatic (i.e., self-recognizing), screen-detected diseases may have more favorable clinicopathological features, leading to better prognosis and better outcome. This paper aims to assess the issue of health care wait screening. It deviates from a decision support system that evaluates the waiting times in diagnostic MI based on operational data from various information systems. Last but not least, one’s assumptions may have an important impact in determining the usefulness of routine laboratory testing at admission. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.},
note = {cited By 1; Conference of 8th International Conference on Big Data Technologies and Applications, BDTA 2017 ; Conference Date: 23 November 2017 Through 24 November 2017; Conference Code:220889},
keywords = {Asymptomatic patients; Human bodies; Laboratory testing; Medical treatment; Operational data; Type of technology; Various technologies; Waiting-time, Big data; Case based reasoning; Computerized tomography; Decision support systems; Health care; Logic programming; Magnetic resonance; Medical imaging, Diagnosis},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva2018516,
title = {Data mining for prediction of length of stay of cardiovascular accident inpatients},
author = {C. Silva and D. Oliveira and H. Peixoto and J. Machado and A. Abelha},
editor = {Alexandrov D. A. Chugunov A.V. Boukhanovsky A.V.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057139340&doi=10.1007%2f978-3-030-02843-5_43&partnerID=40&md5=f8bd9dee0c6500c60b42f061fb2af9c3},
doi = {10.1007/978-3-030-02843-5_43},
issn = {18650929},
year  = {2018},
date = {2018-01-01},
journal = {Communications in Computer and Information Science},
volume = {858},
pages = {516-527},
publisher = {Springer Verlag},
abstract = {The healthcare sector generates large amounts of data on a daily basis. This data holds valuable knowledge that, beyond supporting a wide range of medical and healthcare functions such as clinical decision support, can be used for improving profits and cutting down on wasted overhead. The evaluation and analysis of stored clinical data may lead to the discovery of trends and patterns that can significantly enhance overall understanding of disease progression and clinical management. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a data mining project approach to predict the hospitalization period of cardiovascular accident patients. This provides an effective tool for the hospital cost containment and management efficiency. The data used for this project contains information about patients hospitalized in Cardiovascular Accident’s unit in 2016 for having suffered a stroke. The Weka software was used as the machine learning toolkit. © Springer Nature Switzerland AG 2018.},
note = {cited By 5; Conference of 3rd International Conference on Digital Transformation and Global Society, DTGS 2018 ; Conference Date: 30 May 2018 Through 2 June 2018; Conference Code:220939},
keywords = {Accidents; Decision support systems; Forecasting; Health care; Hospitals; Learning systems, Clinical decision support; Clinical management; Disease progression; Evaluation and analysis; Healthcare sectors; Large amounts of data; Management efficiency; Weka, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Peixoto2018285,
title = {Iron value classification in patients undergoing continuous ambulatory peritoneal dialysis using data mining},
author = {C. Peixoto and H. Peixoto and J. Machado and A. Abelha and M. F. Santos},
editor = {Ziefle M. Bamidis P.D. Bamidis P.D.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052307397&doi=10.5220%2f0006820802850290&partnerID=40&md5=16d6619af21cc9be3469325ea0022e96},
doi = {10.5220/0006820802850290},
isbn = {9789897582998},
year  = {2018},
date = {2018-01-01},
journal = {ICT4AWE 2018 - Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health},
volume = {2018-March},
pages = {285-290},
publisher = {SciTePress},
abstract = {In this article, Data Mining classification techniques are employed, in order to classify as normal or not-normal the iron values from a patients’ blood analysis. The dataset used is relative to patients that were subjected to Continuous Ambulatory Peritoneal Dialysis (CAPD) treatment. Weka software was used for testing several classification algorithms into such data set. The main purpose is finding the best suitable classification algorithm, with a pleasing performance in classifying the instances of the data, whereas preserving low rate of false positives. The IBk algorithm achieved the best performance, being able to correctly classify 97.39% of the instances. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.},
note = {cited By 1; Conference of 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2018 ; Conference Date: 22 March 2018 Through 23 March 2018; Conference Code:135921},
keywords = {Blood analysis; Classification algorithm; Data set; False positive; Low rates; Mining classification; Peritoneal dialysis; Weka, Classification (of information), Data mining; Dialysis; Iron; Patient treatment; Software testing; Statistical tests},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{MacHado2018XIII,
title = {Preface},
author = {J. MacHado and A. Abelha and L. M. Gome and H. Guerra},
editor = {Guerra H. Gomes L.M. Machado J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049987264&partnerID=40&md5=116c0cd6c6ea744c3e309be0ea5c54e2},
isbn = {9789492859037},
year  = {2018},
date = {2018-01-01},
journal = {16th International Industrial Simulation Conference 2018, ISC 2018},
pages = {XIII},
publisher = {EUROSIS},
note = {cited By 0; Conference of 16th International Industrial Simulation Conference, ISC 2018 ; Conference Date: 6 June 2018 Through 8 June 2018; Conference Code:137024},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Prata201828,
title = {Data mining in urgency department: Medical specialty discharge prediction},
author = {M. Prata and H. Peixoto and J. MacHado and A. Abelha},
editor = {Guerra H. Gomes L.M. Machado J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049981921&partnerID=40&md5=3cc8bef2ee13f2fbe0c31b22754c6e21},
isbn = {9789492859037},
year  = {2018},
date = {2018-01-01},
journal = {16th International Industrial Simulation Conference 2018, ISC 2018},
pages = {28-35},
publisher = {EUROSIS},
abstract = {The aim of this paper is to analyze and process a dataset to predict the Medical Specialty (MS) discharge in a hospital Urgency Department (UD). MS discharge is the medical specialty in which a patient gets discharged from the UD. This predictive analysis would improve medical and staff performance, not to mention, less time consuming to the patient and less expensive to both patient and the hospital. However, it is a challenging task due to the quality of data retrieved from UDs that's usually non-treated, with a lot of irrelevant information, sparse and, sometimes, incomplete. This predictive analysis is obtained through Data Mining techniques and machine learning algorithms in Weka environment. It was concluded that feature selection and structured modelling are important factors that affect classification accuracy. It was also concluded as that randomly decreasing or increasing dataset information by varying patient values does not assist directly in increasing accuracy for the prediction of MS discharge. The best results were achieved using the Bagging algorithm with a REPTree classifier and a ten-fold cross-validation, achieving 91.96 % of accuracy and 0.85 F1-score. © 2018 EUROSIS. All rights reserved.},
note = {cited By 2; Conference of 16th International Industrial Simulation Conference, ISC 2018 ; Conference Date: 6 June 2018 Through 8 June 2018; Conference Code:137024},
keywords = {Artificial intelligence; Data mining; Feature extraction; Forecasting; Hospitals; Learning algorithms; Learning systems; Predictive analytics, Bagging algorithms; Classification accuracy; Cross validation; Discharge predictions; F1 scores; Medical specialties; Quality of data; Urgency Department, Medical computing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Moreira201867,
title = {Mobile computing in patient relationship management - A case study},
author = {P. Moreira and D. Oliveira and F. Miranda and A. Abelha and J. MacHado},
editor = {Guerra H. Gomes L.M. Machado J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049957132&partnerID=40&md5=fa01b97bdf11107789ade83cbdf7e6ef},
isbn = {9789492859037},
year  = {2018},
date = {2018-01-01},
journal = {16th International Industrial Simulation Conference 2018, ISC 2018},
pages = {67-71},
publisher = {EUROSIS},
abstract = {Around fourteen years ago, the first hospital in Portugal started to use a patient notification system, to alert patients of their medical events at the hospital, such as appointments, surgeries, exams, treatments through text messages. This notification system is used nowadays, but it faces a big problem: a huge amount of money spent for the telecommunication companies involved. Although each message cost a fraction of cents, it can easily reproduce its value in more than 50,000 euros per year per institution. Since technology and the use of smartphones has been evolving in such a quick way, it is estimated that in no more than 10 years, almost all the Portuguese population will use smartphones or have access to them. For those reasons, the main purpose of the present work, is to design and develop a mobile application in order to substitute the previous notification systems, through push up notifications on the app and by email, that can be saved on the smartphone calendar, translating in no costs associated with the notifications sent by the hospitals. The main motivation is, therefore, suppressing these costs for the hospital, bring the patients closer integrating other systems on the app and make the notification alert more efficient. Thereby, the mobile app will be able, not only to manage each notification and notify the patients, but also to check its medical event history and to schedule medical appointments. © 2018 EUROSIS. All rights reserved.},
note = {cited By 0; Conference of 16th International Industrial Simulation Conference, ISC 2018 ; Conference Date: 6 June 2018 Through 8 June 2018; Conference Code:137024},
keywords = {Costs; eHealth; Hospitals; Information technology; mHealth; Mobile computing; Smartphones, Event history; Mobile app; Mobile applications; Notification systems; Portuguese population; Push notifications; Relationship management; Telecommunication companies, Medical computing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva201849,
title = {Cloud computing environments for simulation of adaptable standardized work and electronic work instructions in industry 4.0},
author = {F. Silva and R. Martins and M. Gomes and A. Silva and J. MacHado and P. Novais and C. Analide},
editor = {Guerra H. Gomes L.M. Machado J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049950235&partnerID=40&md5=ebf4329bee42eedb73be602f1a7d8d22},
isbn = {9789492859037},
year  = {2018},
date = {2018-01-01},
journal = {16th International Industrial Simulation Conference 2018, ISC 2018},
pages = {49-53},
publisher = {EUROSIS},
abstract = {Industry 4.0 is a term connotated with the development of software services able to percept and act in industrial environments. A common characteristic is the availability of data and information in the format of streams which are available for building new industrial processes. In this paper, an architecture of an application able to process different inputs in the creation of electronic work instruction and the optimisation of standard work is presented. Taking into consideration the principles of industry 4.0, the information is stored in a web application which manages user access, content creation and update, review process and optimisation procedures. © 2018 EUROSIS. All rights reserved.},
note = {cited By 5; Conference of 16th International Industrial Simulation Conference, ISC 2018 ; Conference Date: 6 June 2018 Through 8 June 2018; Conference Code:137024},
keywords = {Cloud computing environments; Content creation; Data and information; Industrial environments; Industrial processs; Optimisation procedures; Software services; Work instructions, E-learning, Industry 4.0},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Coelho201893,
title = {Pervasive business intelligence in misericordias – A Portuguese case study},
author = {D. Coelho and T. Guimarães and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Molloy W. O Donoghue J. Maciaszek L.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048998444&doi=10.1007%2f978-3-319-93644-4_5&partnerID=40&md5=b2f1860826d95cf10197295386709390},
doi = {10.1007/978-3-319-93644-4_5},
issn = {18650929},
year  = {2018},
date = {2018-01-01},
journal = {Communications in Computer and Information Science},
volume = {869},
pages = {93-106},
publisher = {Springer Verlag},
abstract = {The healthcare system is one of the main pillars of any society. However, it carries with it an enormous economic weight. Portugal, alongside with many others, is a country in economic and social restructuration and consequently, the need to increase the efficiency of resource management and services is imperative. With the proven effectiveness of Business Intelligence (BI) in many organisations, the urge to implement such tools in Healthcare arises, specifically in the healthcare of Misericórdia. In addition to the results, it presents a critical analysis of the implementation and the process followed for the development and usage of KPIs. In this work, some concepts associated with the use of BI in Misericórdias were addressed, and the architecture of the developed solution was designed. It is also important to emphasise that the solution presented is pervasive, available anywhere at any time. Through this work, it was possible to gather all the data into a single structure (Data Mart), to identify a set of aspects that can be improved and to have a generalised view of the state of operation of the organisation, as far as health care is concerned. The developed includes ten KPIs in the area of Surgery Production and Waiting List Surgery. The dashboards can be analysed in several dimensions: date, specialities, physicians, service, diagnosis, location and time. © 2018, Springer International Publishing AG, part of Springer Nature.},
note = {cited By 1; Conference of 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2017 ; Conference Date: 28 April 2017 Through 29 April 2017; Conference Code:214519},
keywords = {Competitive intelligence; Surgery, Critical analysis; Dashboards; Data mart; Health-care system; Pervasive healthcare; Resource management; Single structure; Waiting lists, Information analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gonçalves2018352,
title = {Step towards a pervasive data system for intensive care medicine},
author = {P. Gonçalves and F. Portela and M. F. Santos and J. Machado and A. Abelha and F. Rua},
editor = {Rocha A. Costanzo S. Adeli H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045347385&doi=10.1007%2f978-3-319-77700-9_35&partnerID=40&md5=137db091342fb3d50c87c2f7ce531156},
doi = {10.1007/978-3-319-77700-9_35},
issn = {21945357},
year  = {2018},
date = {2018-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {747},
pages = {352-362},
publisher = {Springer Verlag},
abstract = {The use of technologies that can facilitate and streamline the processes of those who constantly need to perform various actions or comply with the most varied procedures, requires constant adaptability, either by organizations or users. The focus of this research is precisely the adaptability of technologies and in this case the technology used in the Intensive Care Unit (ICU) of the Centro Hospitalar do Porto (CHP). The increasing use of different electronic devices, all with different characteristics and dimensions, requires the optimization of the platforms that transmit and manipulate all the information, so that it is possible to use it regardless of which device is being used. Through the introduction of new functionalities to the current system, it is intended with this artefact to show the optimization made on the INTCare platform, with the main purpose of increasing its responsiveness. © Springer International Publishing AG, part of Springer Nature 2018.},
note = {cited By 0; Conference of 6th World Conference on Information Systems and Technologies, WorldCIST 2018 ; Conference Date: 27 March 2018 Through 29 March 2018; Conference Code:212469},
keywords = {Data systems; Electronic device; INTCare; Intensive care medicines; Pervasive environments; Pervasive healths, Information systems; Information use; mHealth; Ubiquitous computing, Intensive care units},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Esteves2018195,
title = {Mobile collaborative augmented reality and business intelligence: A system to support elderly people’s self-care},
author = {M. Esteves and F. Miranda and J. Machado and A. Abelha},
editor = {Rocha A. Costanzo S. Adeli H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045332393&doi=10.1007%2f978-3-319-77700-9_20&partnerID=40&md5=a0502f7393a6d43cf3dc1aaab0ae8484},
doi = {10.1007/978-3-319-77700-9_20},
issn = {21945357},
year  = {2018},
date = {2018-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {747},
pages = {195-204},
publisher = {Springer Verlag},
abstract = {The ageing of the population increases the number of elderly people dependent in self-care. Thus, being dependent in a home context is a fact that deserves attention from social support entities integrated into the community, such as nursing homes. In this sense, this study is aimed at elderly dependent people in self-care, their caregivers, and members of nursing teams, and emerged to ensure predominantly the continuity of care of patients from Portuguese nursing homes and to strengthen the communication strategies between the different elements of the target audience. Therefore, at this stage of the project, the design of a preliminary archetype of a mobile collaborative augmented reality and business intelligence system is proposed, which main objectives are to accompany, teach, and share information between its users. It will be a reinforcement, that is, a way to promote and complete the knowledge and skills to deal with patients’ health. © Springer International Publishing AG, part of Springer Nature 2018.},
note = {cited By 14; Conference of 6th World Conference on Information Systems and Technologies, WorldCIST 2018 ; Conference Date: 27 March 2018 Through 29 March 2018; Conference Code:212469},
keywords = {Augmented reality; Competitive intelligence; Data warehouses; Hospitals; Information analysis; Information systems; Information use; mHealth; Nursing; Telemedicine, Collaborative learning; Elderly people; Health informations; Nursing homes; Self-care; Telenursing, Home health care},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2018205,
title = {New approach to an openEHR introduction in a portuguese healthcare facility},
author = {D. Oliveira and A. Coimbra and F. Miranda and N. Abreu and P. Leuschner and J. Machado and A. Abelha},
editor = {Rocha A. Costanzo S. Adeli H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045320739&doi=10.1007%2f978-3-319-77700-9_21&partnerID=40&md5=6a27b6dfc2a5b4b098597344eb26de39},
doi = {10.1007/978-3-319-77700-9_21},
issn = {21945357},
year  = {2018},
date = {2018-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {747},
pages = {205-211},
publisher = {Springer Verlag},
abstract = {Implementing a new EHR data system is not easy, as the systems already in place and user mentality are very difficult to change. The openEHR architecture introduces a new way of organizing clinical information using archetypes and templates. The present paper focuses on the initial steps of the implementation of an openEHR based EHR in a Portuguese major HealthCare provider. The system comprises operational templates creation through the creation of a validation mechanism and after that storage, a platform for data generation dynamically constructed from templates and an interoperability mechanism through the implementation of an HL7 V3/CDA message system. © Springer International Publishing AG, part of Springer Nature 2018.},
note = {cited By 8; Conference of 6th World Conference on Information Systems and Technologies, WorldCIST 2018 ; Conference Date: 27 March 2018 Through 29 March 2018; Conference Code:212469},
keywords = {Archetypes; HL7 V3/CDA; openEHR; Operational templates; SNOMED-CT, Digital storage; Health care; Information systems; Information use, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Loreto2017170,
title = {Improving maternity care with business intelligence},
author = {P. Loreto and F. Fonseca and A. Morais and H. Peixoto and A. Abelha and J. Machado},
editor = {Younas M. Portela F. Awan I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047413573&doi=10.1109%2fFiCloudW.2017.89&partnerID=40&md5=54a43f1b7c10b42ab82c19d054c3a987},
doi = {10.1109/FiCloudW.2017.89},
isbn = {9781538632819},
year  = {2017},
date = {2017-01-01},
journal = {Proceedings - 2017 5th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017},
volume = {2017-January},
pages = {170-177},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The aim of this paper is to develop clinical indicators for obstetrics through the use of Business Intelligence (BI) tools, since valid and reliable clinical indicators can help measuring quality of healthcare services and support decision-making processes. This paper gives an overview of concepts related to Health Information Systems (HIS) and BI, along with some related work to highlight the advantages that BI solutions can bring when applied to healthcare. In this paper is also presented the data warehousing and the ETL process, that was necessary for the development of indicators and which is usually hidden from endusers, is described. The indicators were developed using Power BI and were analysed and compared with reference values from both national and international health reports. The discussion of the developed indicators made it possible to measure the quality of the obstetrics service, to identify the problematic areas and to decide whether improvement measures should be taken. © 2017 IEEE.},
note = {cited By 10; Conference of 5th IEEE International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017 ; Conference Date: 21 August 2017 Through 23 August 2017; Conference Code:132622},
keywords = {Competitive intelligence; Data warehouses; Decision making; Health care; Indicators (instruments); Information analysis; Information systems; Information use; Internet of things; Medical computing; Obstetrics; Public health; Warehouses, Decision making process; Extract transform loads; Health information systems; Improvement measure; International healths; Quality of health care; Reference values; Related works, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Peixoto2017178,
title = {Continuous ambulatory peritoneal dialysis: Business intelligence applied to patient monitoring: CAPD study and statistics},
author = {C. Peixoto and C. Brito and M. Fontainhas and H. Peixoto and J. Machado and A. Abelha},
editor = {Younas M. Portela F. Awan I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047407906&doi=10.1109%2fFiCloudW.2017.91&partnerID=40&md5=52837dd5542ae72ec58b161ef95de992},
doi = {10.1109/FiCloudW.2017.91},
isbn = {9781538632819},
year  = {2017},
date = {2017-01-01},
journal = {Proceedings - 2017 5th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017},
volume = {2017-January},
pages = {178-185},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Continuous Ambulatory Peritoneal Dialysis (CAPD) is one of the many treatments for patients with advanced kidney disease. It is a treatment that needs regular monitoring and understanding of all the factors of blood and urine samples of each patient to understand if the treatment is going well. This article will explore data information from patients undergoing CAPD procedure. This data information helps to comprehend how interoperability acts in a Health Information System since this data contains patients' personal information but also patients' blood and urine samples' results, meaning all the services must be connected. In this work, it is used Business Intelligence process to prove that all the information available can be useful to understand the treatment above-mentioned and also how can several factors influence or not the number of patients going through kidney failure and CAPD by the study of indicators. © 2017 IEEE.},
note = {cited By 0; Conference of 5th IEEE International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017 ; Conference Date: 21 August 2017 Through 23 August 2017; Conference Code:132622},
keywords = {Blood and urine samples; Data informations; Health information systems; Kidney; Kidney disease; Peritoneal dialysis; Personal information, Blood; Competitive intelligence; Dialysis; Information analysis; Information dissemination; Information systems; Information use; Internet of things; Interoperability; Microarrays; Patient monitoring, Patient treatment},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva2017186,
title = {Business intelligence for cardiovascular disease assessment},
author = {C. Silva and J. Pereira and L. Costa and H. Peixoto and J. Machado and A. Abelha},
editor = {Younas M. Portela F. Awan I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047267736&doi=10.1109%2fFiCloudW.2017.90&partnerID=40&md5=ccda0d09b5fb47a4fa0bf32b4235413c},
doi = {10.1109/FiCloudW.2017.90},
isbn = {9781538632819},
year  = {2017},
date = {2017-01-01},
journal = {Proceedings - 2017 5th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017},
volume = {2017-January},
pages = {186-193},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The healthcare industry has historically generated large amounts of data for various reasons, from simple record keeping to improving patient care with foreknowledge of the subject's own medical history, not to mention the in-formation required for the organizations day-to-day operations in order to follow its' own compliance and regulatory requirements. This project primarily consists in the development of a Data Warehouse in order to transform the abundant and heterogeneous clinical data in a single multidimensional structure capable of responding promptly to the information consulting needs with no redundancy. The data being treated contains information about patients hospitalized in Cardiovascular Accidents' unit in 2016 for having suffered a cardiovascular accident. The final goal of this project is the construction of indicators using interactive data visualization BI tools through Power BI. With these indicators, it was made an analysis of multidimensional data interactively from multiple perspectives and a comparison between that data and statistics obtained from different studies. © 2017 IEEE.},
note = {cited By 1; Conference of 5th IEEE International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017 ; Conference Date: 21 August 2017 Through 23 August 2017; Conference Code:132622},
keywords = {Accidents, Cardio-vascular disease; Cerebrovascular accident; Day-to-day operations; Health information systems; Large amounts of data; Multi-dimensional structure; Multidimensional data; Regulatory requirements, Competitive intelligence; Data visualization; Data warehouses; Information analysis; Internet of things; Patient treatment; Records management; Regulatory compliance},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2017194,
title = {Improving nursing practice through interoperability and intelligence},
author = {D. Oliveira and J. Duarte and A. Abelha and J. Machado},
editor = {Younas M. Portela F. Awan I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047248056&doi=10.1109%2fFiCloudW.2017.92&partnerID=40&md5=f295b7a271642cfe46029f4e7babeca0},
doi = {10.1109/FiCloudW.2017.92},
isbn = {9781538632819},
year  = {2017},
date = {2017-01-01},
journal = {Proceedings - 2017 5th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017},
volume = {2017-January},
pages = {194-199},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Hospital inpatient care compromises one of the most demanding services in health institutions when providing a careful and continuous healthcare assistance. Such demands require constant update of the patients' electronic health record allied with support systems responsible for monitoring their clinical information. In this context, this paper presents a new web platform for daily monitoring of patients, designed to be used by health professionals, especially nurses. The application is based on React, an open-source JavaScript library for building user interfaces. The developed tool incorporates two main features: the real-time visualization of the data, and the storage of the patient's historic during an inpatient care episode. The storage capability allows keeping the data updated among hospital shifts. Moreover, this work also highlights the required adaptability of this platform for each health units inside a hospital center according with its needs. © 2017 IEEE.},
note = {cited By 3; Conference of 5th IEEE International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017 ; Conference Date: 21 August 2017 Through 23 August 2017; Conference Code:132622},
keywords = {Building users; Clinical information; Electronic health record; Health professionals; Hospital information systems; Real time visualization; Storage capability; Support systems, Data visualization; Digital storage; Health care; Hospitals; Internet of things; Nursing; User interfaces, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Veloso201722,
title = {Categorize readmitted patients in intensive medicine by means of clustering data mining},
author = {R. Veloso and F. Portela and M. F. Santos and J. Machado and A. Da Silva Abelha and F. Rua and A. Silva},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020201969&doi=10.4018%2fIJEHMC.2017070102&partnerID=40&md5=1a014616a1e34f2ec5c1f84b981988f1},
doi = {10.4018/IJEHMC.2017070102},
issn = {1947315X},
year  = {2017},
date = {2017-01-01},
journal = {International Journal of E-Health and Medical Communications},
volume = {8},
number = {3},
pages = {22-37},
publisher = {IGI Global},
abstract = {With a constant increasing in the health expenses and the aggravation of the global economic situation, managing costs and resources in healthcare is nowadays an essential point in the management of hospitals. The goal of this work is to apply clustering techniques to data collected in real-Time about readmitted patients in Intensive Care Units in order to know some possible features that affect readmissions in this area. By knowing the common characteristics of readmitted patients it will be possible helping to improve patient outcome, reduce costs and prevent future readmissions. In this study, it was followed the Stability and Workload Index for Transfer (SWIFT) combined with the results of clinical tests for substances like lactic acid, leucocytes, bilirubin, platelets and creatinine. Attributes like sex, age and identification if the patient came from the chirurgical block were also considered in the characterization of potential readmissions. In general, all the models presented very good results being the Davies-Bouldin index lower than 0.82, where the best index was 0.425. Copyright © 2017, IGI Global.},
note = {cited By 7},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Portela2017491,
title = {An online-processing critical patient monitoring system- an interoperability overview},
author = {F. Portela and F. Miranda and M. F. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022060526&doi=10.2298%2fCSIS160604013P&partnerID=40&md5=083a498e7544609948c6931c09836d3d},
doi = {10.2298/CSIS160604013P},
issn = {18200214},
year  = {2017},
date = {2017-01-01},
journal = {Computer Science and Information Systems},
volume = {14},
number = {2},
pages = {491-515},
publisher = {ComSIS Consortium},
abstract = {With the increasing expansion of Healthcare Information Systems, platforms for interoperability and monitoring systems have become vital tools to support the clinical practice. Under this scenario, the creation of knowledge in real-time to feed decision support tools is essential. INTCare is one the solutions that adapts the way that information is gathered and processed in order to obtain that knowledge. Once new information arrives, it triggers the ETL (extract, transform, load) procces enabling real-time processes like data mining. However, the system fails in recognize if a patient is absent from bed or not. This problem led to the development of the Patient Localization and Management System (PaLMS), a Radio-Frequency IDentification (RFID) serving as localization and monitoring system. This paper describes the PaLMS implementation as well as an intelligent Multi- Agent System for the integration of PaLMS into the hospital platform for interoperability named AIDA (Agency for Integration, Diffusion and Archive of Medical Information). At the end, an usability evaluation was performed in order to assess the level of usability of the existing systems at Centro Hospitalar do Porto, such as the PaLMS, the INTCare and the AIDA platform. In terms of usability, the system got very positive evalutions, despite the fact some medical staff argued that they lose too much time elaborating the records. © 2017 ComSIS Consortium. All rights reserved.},
note = {cited By 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inbook{Abelha2017840,
title = {Improving quality of services in maternity care triage system},
author = {A. Abelha and E. Pereira and A. Brandão and F. Portela and M. F. Santos and J. Machado and J. Braga},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021335385&doi=10.4018%2f978-1-5225-2237-9.ch039&partnerID=40&md5=a0f1e3f7081e8a1cf4d25302cd1ce809},
doi = {10.4018/978-1-5225-2237-9.ch039},
isbn = {9781522522393; 1522522379; 9781522522386},
year  = {2017},
date = {2017-01-01},
journal = {Healthcare Ethics and Training: Concepts, Methodologies, Tools, and Applications},
volume = {2},
pages = {840-859},
publisher = {IGI Global},
abstract = {The main objectives in triage are to improve the quality of care and reduce the risks associated to the waiting time in emergency care. Thus, an efficient triage is a good way to avoid some future problems and how much quicker it is, more the patient can benefit. The most common triage system is the Manchester Triage System that is a reliable system focused in the emergency department of a hospital. However, its use is more suitable for more widespread medical emergencies and not for specialized cases, like Gynecological and Obstetrics emergencies. To overcome these limitations, an alternative pre-triage system, integrated into an intelligent decision support system, was developed in order to better characterize the patient and correctly defined her as urgent or not. This system allows the increase of patient's safety, especially women who need immediate care. This paper includes the workflow that describes the decision process in real time in the emergency department, when women are submitted to triage and identify points of evolution. © 2017, IGI Global.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Carvalhosa2017,
title = {Good practices in Local Government - A first overview of Portuguese reality},
author = {P. Carvalhosa and F. Portela and J. Machado and M. F. Santos and A. Abelha},
editor = {Sujarwo A. Hidayat T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017443786&doi=10.1088%2f1757-899X%2f185%2f1%2f012002&partnerID=40&md5=eec55330dba43720410914cbbb68f7f8},
doi = {10.1088/1757-899X/185/1/012002},
issn = {17578981},
year  = {2017},
date = {2017-01-01},
journal = {IOP Conference Series: Materials Science and Engineering},
volume = {185},
number = {1},
publisher = {Institute of Physics Publishing},
abstract = {Good practices in eGov are being increasingly used by Local Governments being that it is considered by them as an advantage. The main goal is providing to the town hall a differentiation point and approximate their services to the citizens. For this, it is necessary to define and apply innovative strategies in order to increase the use of services by the citizens. This paper is framed in a research work and it presents a first overview of the existing good practices in eGov, taking in consideration the Portuguese's reality. The good practices identified were distinguished with many awards and with a positive response from the target audience. The use of digital marketing strategies aims to increase their membership and coming closer the municipalities of its citizens through the dissemination of the good practices. At this moment the data collected are almost exclusively of good practice in Portugal, however some international practices were also identified. As a result of this study the community has a list of good practices that can be applied in their municipalities. © Published under licence by IOP Publishing Ltd.},
note = {cited By 2; Conference of 1st Annual International Conference on Information Technology and Digital Applications 2016, ICITDA 2016 ; Conference Date: 14 November 2016 Through 16 November 2016; Conference Code:127047},
keywords = {Condensed matter physics; Engineering; Industrial engineering; Materials science, Digital marketing; Good practices; Innovative strategies; International practice; Local government; Portugal; Target audience, Marketing},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Reis201741,
title = {Machine Learning in Nutritional Follow-up Research},
author = {R. Reis and H. Peixoto and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052023664&doi=10.1515%2fcomp-2017-0008&partnerID=40&md5=6535e510ffa66e106037cdc4c24125a5},
doi = {10.1515/comp-2017-0008},
issn = {22991093},
year  = {2017},
date = {2017-01-01},
journal = {Open Computer Science},
volume = {7},
number = {1},
pages = {41-45},
publisher = {Walter de Gruyter GmbH},
abstract = {Healthcare is one of the world’s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being ‘information rich’ yet ‘knowledge poor’. Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. The aim of this project was to predict if a patient would need to be followed by a nutrition specialist, by combining a nutritional dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%. © 2017 R. Reis et al.},
note = {cited By 15},
keywords = {Best decision; Decision makers; Follow up; Healthcare organizations; Knowledge-poor; Large volumes; Mining classification, Classification (of information); Decision making; Large dataset; Machine learning; Nutrition, Data mining},
pubstate = {published},
tppubtype = {article}
}
@article{Pereira201736,
title = {A Data Mining Approach for Cardiovascular Diagnosis},
author = {J. Pereira and H. Peixoto and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052011971&doi=10.1515%2fcomp-2017-0007&partnerID=40&md5=162e5f28bc83a0bc23ae1549d6584df6},
doi = {10.1515/comp-2017-0007},
issn = {22991093},
year  = {2017},
date = {2017-01-01},
journal = {Open Computer Science},
volume = {7},
number = {1},
pages = {36-40},
publisher = {Walter de Gruyter GmbH},
abstract = {The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD) unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA) machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets. © 2017 J. Pereira et al},
note = {cited By 7},
keywords = {Accidents; Decision making; Diagnosis; Health care; Hospitals; Machine learning, Cardio-vascular disease; Cardiovascular diagnosis; Healthcare industry; Knowledge analysis; Large amounts; Large amounts of data; Machine learning methods; Quality of life, Data mining},
pubstate = {published},
tppubtype = {article}
}
@article{Ribeiro2017,
title = {Patients' admissions in intensive care units: A clustering overview},
author = {A. Ribeiro and F. Portela and M. Santos and A. Abelha and J. Machado and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014216800&doi=10.3390%2finfo8010023&partnerID=40&md5=851d20dc86a037782c7a789bd21e6818},
doi = {10.3390/info8010023},
issn = {20782489},
year  = {2017},
date = {2017-01-01},
journal = {Information (Switzerland)},
volume = {8},
number = {1},
publisher = {MDPI AG},
abstract = {Intensive care is a critical area of medicine having a multidisciplinary nature requiring all types of healthcare professionals. Given the critical environment of intensive care units (ICUs), the need to use information technologies, like decision support systems, to improve healthcare services and ICU management is evident. It is proven that unplanned and prolonged admission to the ICU is not only prejudicial to a patient's health, but also such a situation implies a readjustment of ICU resources, including beds, doctors, nurses, financial resources, among others. By discovering the common characteristics of the admitted patients, it is possible to improve these outcomes. In this study clustering techniques were applied to data collected from admitted patients in an intensive care unit. The best results presented a silhouette of 1, with a distance to centroids of 6.2 × 10-17 and a Davies-Bouldin index of -0.652.},
note = {cited By 5},
keywords = {Admissions; Clustering; Clustering techniques; Critical environment; Davies-Bouldin index; Health care professionals; INTCare system; Intensive care; INTcare system, Artificial intelligence; Data mining; Decision support systems; Health care; Information management, Intensive care units},
pubstate = {published},
tppubtype = {article}
}
@article{DaSilvaPortela2017179,
title = {Real-time intelligent decision support and monitoring system of critical patients [Sistema inteligente de apoio à decisão e monitorização de doentes críticos em tempo-real]},
author = {C. F. Da Silva Portela and M. F. V. T. Dos Santos and A. C. Da Silva Abelha and J. M. Machado and Á. M. Silva and F. R. Martins},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077303942&doi=10.1159%2f000486146&partnerID=40&md5=a6203c54eae88d9b8f243d5ae6eb8281},
doi = {10.1159/000486146},
issn = {25043145},
year  = {2017},
date = {2017-01-01},
journal = {Portuguese Journal of Public Health},
volume = {35},
number = {3},
pages = {179-192},
publisher = {S. Karger AG},
abstract = {Intensive care units are places where patients’ vital signs are continuously monitored and recorded alongside a multiplicity of clinical parameters. The main goal of this work is to study and develop an intelligent system to promote new decision-making knowledge crucial to provide better treatment to the patient. This article presents the achieved goals; in particular, the system developed for monitoring the clinical data and, using data mining technologies, for predicting clinical events with great sensitivity (90–100%), including organ failure probability, read-missions, and sepsis. © 2018 The Author(s).},
note = {cited By 3},
keywords = {Article; clinical decision support system; clinical feature; critical illness; data mining; hospital readmission; human; multiple organ failure; patient monitoring; real time intelligent decision support; sepsis},
pubstate = {published},
tppubtype = {article}
}
@article{Foshch201711,
title = {Comparison of two control programs of the VVER-1000 nuclear power unit using regression data mining models},
author = {T. Foshch and J. Machado and F. Portela and M. Maksimov and O. Maksimova},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043974669&doi=10.32918%2fnrs.2017.3%2875%29.02&partnerID=40&md5=4312745dd5bff79efcaaba8959d122e6},
doi = {10.32918/nrs.2017.3(75).02},
issn = {20736231},
year  = {2017},
date = {2017-01-01},
journal = {Nuclear and Radiation Safety},
volume = {3},
number = {75},
pages = {11-17},
publisher = {State Scientific and Technical Center for Nuclear and Radiation Safety},
abstract = {A load-following mode of nuclear power plants (NPP) is a complicated procedure because there are significant changes in many interrelated processes. In order to show which control program (CP) of NPP is better to use, data mining (DM) techniques can be introduced. This study proposes a DM approach in order to show a possibility of using DM regression models for NPP. The datasets for DM were obtained by simulating two static CP of VVER-1000 NPP in Simulink software of Matlab program package. © T. Foshch, J. Machado, F. Portela, M. Maksimov, O. Maksimova, 2017.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inbook{Cardoso2017689,
title = {Interoperability in healthcare},
author = {L. Cardoso and F. Marins and C. Quintas and F. Portela and M. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041678365&doi=10.4018%2f978-1-5225-3926-1.ch036&partnerID=40&md5=fd0a89840184af4760ca852b42571ff9},
doi = {10.4018/978-1-5225-3926-1.ch036},
isbn = {9781522539285; 1522539263; 9781522539261},
year  = {2017},
date = {2017-01-01},
journal = {Health Care Delivery and Clinical Science: Concepts, Methodologies, Tools, and Applications},
pages = {689-714},
publisher = {IGI Global},
abstract = {With the advancement of technology, patient information has been being computerized in order to facilitate the work of healthcare professionals and improve the quality of healthcare delivery. However, there are many heterogeneous information systems that need to communicate, sharing information and making it available when and where it is needed. To respond to this requirement the Agency for Integration, Diffusion, and Archiving of medical information (AIDA) was created, a multi-agent and service-based platform that ensures interoperability among healthcare information systems. In order to improve the performance of the platform, beyond the SWOT analysis performed, a system to prevent failures that may occur in the platform database and also in machines where the agents are executed was created. The system has been implemented in the Centro Hospitalar do Porto (one of the major Portuguese hospitals), and it is now possible to define critical workload periods of AIDA, improving high availability and load balancing. This is explored in this chapter. © 2018, IGI Global.},
note = {cited By 10},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Neto20171104,
title = {Knowledge Discovery from Surgical Waiting lists},
author = {C. Neto and H. Peixoto and V. Abelha and A. Abelha and J. Machado},
editor = {Peppard J. Varajao J.E. Cruz-Cunha M.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040217774&doi=10.1016%2fj.procs.2017.11.141&partnerID=40&md5=f1a8b6d40ae079b7d7e26aef6b8e7e7c},
doi = {10.1016/j.procs.2017.11.141},
issn = {18770509},
year  = {2017},
date = {2017-01-01},
journal = {Procedia Computer Science},
volume = {121},
pages = {1104-1111},
publisher = {Elsevier B.V.},
abstract = {Methods for knowledge discovery in data bases (KDD) have been studied for more than a decade. New methods are required owing to the size and complexity of data collections in administration, business and science. They include procedures for data query and extraction, for data cleaning, data analysis, and methods of knowledge representation. The part of KDD dealing with the analysis of the data has been termed data mining. Data mining is the process of pattern discovery and extraction where huge amount of data is involved. Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. In this work is presented an approach for the use of data mining in the context of waiting lists for surgery, namely for predicting the type of surgery (programmed or additional) for a record in the list. © 2017 The Authors. Published by Elsevier B.V.},
note = {cited By 9; Conference of International Conference on ENTERprise Information Systems, CENTERIS 2017, International Conference on Project MANagement, ProjMAN 2017 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2017 ; Conference Date: 8 November 2017 Through 10 November 2017; Conference Code:133143},
keywords = {Artificial intelligence; Classification (of information); Decision support systems; Extraction; Health care; Information management; Information systems; Knowledge representation; Project management; Surgery, Data collection; Data mining applications; Healthcare industry; Knowledge discovery in data basis; Knowledge discovery in database; Pattern discovery; Surgical waiting lists; Waiting lists, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Rodrigues2017591,
title = {Understanding Stroke in Dialysis and Chronic Kidney Disease},
author = {M. Rodrigues and H. Peixoto and M. Esteves and J. Machado and Abelha},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033482094&doi=10.1016%2fj.procs.2017.08.296&partnerID=40&md5=7da878098027c0cfd48debf22d157457},
doi = {10.1016/j.procs.2017.08.296},
issn = {18770509},
year  = {2017},
date = {2017-01-01},
journal = {Procedia Computer Science},
volume = {113},
pages = {591-596},
publisher = {Elsevier B.V.},
abstract = {Patients with severe kidney failure need to be carefully monitored. One of the many treatments is called Continuous Ambulatory Peritoneal Dialysis (CAPD). This kind of treatment intends to maintain the blood tests as normal as possible. Data Mining and Machine Learning can take a simple and meaningless blood's test data set and build it into a Decision Support System. Through this article, Machine Learning algorithms will be explored with different Data Mining Models in order to extract knowledge and classify a patient with a stroke risk or not, according to their blood analysis. Peer-review under responsibility of the Conference Program Chairs. © 2017 The Authors. Published by Elsevier B.V.},
note = {cited By 11; Conference of 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2017 and the 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2017 ; Conference Date: 18 September 2017 Through 20 September 2017; Conference Code:130912},
keywords = {Artificial intelligence; Blood; Classification (of information); Data communication systems; Decision support systems; Dialysis; Health care; Learning algorithms; Learning systems; Risk assessment; Statistical tests, Blood analysis; Blood test; Chronic kidney disease; Conference programs; Data mining models; Peer review; Peritoneal dialysis; Test data, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Fonseca2017565,
title = {Step Towards Prediction of Perineal Tear},
author = {F. Fonseca and H. Peixoto and F. Miranda and J. Machado and A. Abelha},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033453791&doi=10.1016%2fj.procs.2017.08.284&partnerID=40&md5=48288649c62db1ddede9cdc0711b5b5d},
doi = {10.1016/j.procs.2017.08.284},
issn = {18770509},
year  = {2017},
date = {2017-01-01},
journal = {Procedia Computer Science},
volume = {113},
pages = {565-570},
publisher = {Elsevier B.V.},
abstract = {The aim of this study is to predict, through data mining tools, the incidence of perineal tear. This kind of laceration developed during child delivery might imply surgery and entails a set of several consequences. Clinical Decision Support Systems, with the information collected from patients' electronic health records combined with the data mining techniques, may decrease the incidence of perineal tears during labour. Peer-review under responsibility of the Conference Program Chairs. © 2017 The Authors. Published by Elsevier B.V.},
note = {cited By 6; Conference of 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2017 and the 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2017 ; Conference Date: 18 September 2017 Through 20 September 2017; Conference Code:130912},
keywords = {Artificial intelligence; Data communication systems; Decision support systems; Health care; Obstetrics, Clinical decision support systems; Conference programs; Data-mining tools; Electronic health record; Peer review; Perineal Tear, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Morais2017571,
title = {Predicting the need of Neonatal Resuscitation using Data Mining},
author = {A. Morais and H. Peixoto and C. Coimbra and A. Abelha and J. Machado},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033438494&doi=10.1016%2fj.procs.2017.08.287&partnerID=40&md5=ca5be854d7ed2e4bdfaeb31122117f0e},
doi = {10.1016/j.procs.2017.08.287},
issn = {18770509},
year  = {2017},
date = {2017-01-01},
journal = {Procedia Computer Science},
volume = {113},
pages = {571-576},
publisher = {Elsevier B.V.},
abstract = {It is estimated that approximately 10% of newborns require some kind of assistance for breathing at birth. Aiming to prevent neonatal mortality, the goal behind this paper is to predict the need for neonatal resuscitation given some health conditions of both the newborn and the mother, and also the characteristics of the pregnancy and the delivery using Data Mining (DM) models induced with classification techniques. During the DM process, the CRISP-DM Methodology was followed and the WEKA software tool was used to induce the DM models. For some models, it was possible to achieve sensitivity results higher than 90% and specificity and accuracy results superior to 98%, which were considered to be satisfactory. Peer-review under responsibility of the Conference Program Chairs. © 2017 The Authors. Published by Elsevier B.V.},
note = {cited By 19; Conference of 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2017 and the 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2017 ; Conference Date: 18 September 2017 Through 20 September 2017; Conference Code:130912},
keywords = {Artificial intelligence; Classification (of information); Data mining; Decision support systems; Health care; Resuscitation, Classification technique; Conference programs; CRISP-DM; Data mining models; Health condition; Neonatal Resucitation; Peer review, Data communication systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Coelho2017117,
title = {A pervasive business intelligence solution to manage Portuguese misericordia},
author = {D. Coelho and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Ziefle M. O'Donoghue J. Rocker C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025135279&doi=10.5220%2f0006382301170123&partnerID=40&md5=27420fb3b5ae5b86225ef7a846c6c1cf},
doi = {10.5220/0006382301170123},
isbn = {9789897582516},
year  = {2017},
date = {2017-01-01},
journal = {ICT4AWE 2017 - Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health},
pages = {117-123},
publisher = {SciTePress},
abstract = {Currently, the healthcare system is one of the main pillars of any society. Given the economic crisis in Portugal and poor healthcare system in need of profound improvements, the need to increase the efficiency of resource management and services is imperative. With the increasing use of Business Intelligence (BI) in organisations and the proven effectiveness of this, comes the desire to use BI in healthcare, specifically in the healthcare of Misericórdia. One of the purposes of this article is to present the results obtained through the development of the dissertation whose theme is "Prototyping of Business Intelligence component to support the management in the health area of a Misericórdia". So, in this work, some concepts associated with the use of BI in Misericórdias were addressed, and the Pervasive BI architecture of the developed solution was designed. It is also important to emphasise that the solution presented is pervasive, available anywhere at any time. Furthermore, a set of metrics were developed and the data presented in the form of dashboards, for later use by the users. Through this work, it was possible to gather all the data into a single structure (Data Mart), to identify a set of aspects that can be improved and to have a generalised view of the state of operation of the organisation, as far as health care is concerned.l. © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
note = {cited By 1; Conference of 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2017 ; Conference Date: 28 April 2017 Through 29 April 2017; Conference Code:128065},
keywords = {Competitive intelligence; Information analysis, Dashboards; Data mart; Economic crisis; Health-care system; Misericordia; Pervasive healthcare; Resource management; Single structure, Health care},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Queirós2017417,
title = {Magni - A framework for developing context-aware mobile applications},
author = {R. Queirós and F. Portela and J. Machado},
editor = {Reis L. P. Rocha A. Costanzo S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018578020&doi=10.1007%2f978-3-319-56541-5_43&partnerID=40&md5=1dbb20a154788464797e2ac8b870f5a7},
doi = {10.1007/978-3-319-56541-5_43},
issn = {21945357},
year  = {2017},
date = {2017-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {571},
pages = {417-426},
publisher = {Springer Verlag},
abstract = {The advent of Internet and ubiquitous technologies has been fostering the appearance of intelligent mobile applications aware of their environment and the objects nearby. Despite its popularity, mobile devel-opers are often required to write large and disorganized amounts of code, mixing UI with business logic and interact, in a ad-hoc fashion, with sensor devices and services. These habits hinder the code maintenance, refactoring and testing, while negatively influencing the consistency and performance of mobile applications. In this paper we present Magni as an abstract framework for the design and implementation of personalized and context-aware mobile applications. The corner stone of the frame-work is its architectural pattern based on the Model–View–Presenter pattern in the UI layer relying in REST services the majority of the app features. This paradigm fosters the modular design, implementing the separation of concerns concept and allowing an easier implementation of unit tests. In order to validate the framework, we present a prototype for an healthcare automotive app. The main goal of the app is to facilitate the access to health related points of interest such as hospitals, clinics and pharmacies. © Springer International Publishing AG 2017.},
note = {cited By 2; Conference of 5th World Conference on Information Systems and Technologies, WorldCIST ; Conference Date: 11 April 2017 Through 13 April 2017; Conference Code:190479},
keywords = {Architectural pattern; Context-aware mobile application; Design and implementations; Design Patterns; Geolocalization; Mobile frameworks; Separation of concerns concepts; Ubiquitous technology, Health care; Information systems; Information use; mHealth; Mobile computing; Web services, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva2017305,
title = {An ontology for mapping cerebral death},
author = {A. Silva and F. Portela and M. F. Santos and A. Abelha and J. Machado},
editor = {Reis L. P. Rocha A. Costanzo S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018571075&doi=10.1007%2f978-3-319-56541-5_31&partnerID=40&md5=251b63573ddf0db18ec446964adb2165},
doi = {10.1007/978-3-319-56541-5_31},
issn = {21945357},
year  = {2017},
date = {2017-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {571},
pages = {305-311},
publisher = {Springer Verlag},
abstract = {Brain death is one of the most serious diagnoses that can be diagnosed in a patient. The possibility to detect it before its happening is one of the possible steps for the prevention of this event. The x-rays – Computed Tomography scans, are a very important test for the detection of this diagnosis. This paper proposes the use of an ontology on the registration of x-rays made to patients. This work was performed through the data provided by the Centro Hospitalar do Porto - Hospital de Santo António. The ontology was used based on an analysis made to the data and with the use of a dictionary developed in the same analysis. Finally, we added to the ontology the types of patients with brain death that were discov ered in a previous work that used the dictionary that is present in this ontology. © Springer International Publishing AG 2017.},
note = {cited By 1; Conference of 5th World Conference on Information Systems and Technologies, WorldCIST ; Conference Date: 11 April 2017 Through 13 April 2017; Conference Code:190479},
keywords = {Brain death; Computed tomography scan; NAtural language processing; Text mining, Computerized tomography; Data mining; Diagnosis; Information systems; Medicine; Natural language processing systems; X rays, Ontology},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marins2017407,
title = {An agent-based rfid monitoring system for healthcare},
author = {F. Marins and L. Cardoso and M. Esteves and J. Machado and A. Abelha},
editor = {Reis L. P. Rocha A. Costanzo S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018537705&doi=10.1007%2f978-3-319-56541-5_42&partnerID=40&md5=df9f34cfed8f1b586a3fc6adb6bdb324},
doi = {10.1007/978-3-319-56541-5_42},
issn = {21945357},
year  = {2017},
date = {2017-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {571},
pages = {407-416},
publisher = {Springer Verlag},
abstract = {In the last years, with the progressive expansion of Healthcare Information Systems (HISs), the healthcare platforms for interoperability and monitoring systems have become increasingly more vital sources of clinical information. In this context, in Centro Hospitalar do Porto (CHP), the INTCare system was developed with the purpose to create new useful knowledge for deci sion support in real-time. It is an unquestionable potential area to develop effective systems for the prediction of clinical events, including Decision Support Systems (DSSs), for organ failure and death in Intensive Care. The INTCare uses multiple data sources that are collected at the bedside. However, this system fails on the recognition of the patient absence in bed. Thereby, this problem led to the devel opment of the Patient Localization and Management System (PaLMS), i.e., a RFID localization and monitoring system. Thus, this paper describes the PaLMS Intelligent Multi-Agent System for the integration of PaLMS into the hospital platform for Interoperability, Diffusion and Archive – Agency for Integration, Diffusion and Archive of Medical Information (AIDA) platform. On the other hand, a failure prevention system that actuates in the PaLMS agents, improving their availability, is also presented and thoroughly discussed. © Springer International Publishing AG 2017.},
note = {cited By 7; Conference of 5th World Conference on Information Systems and Technologies, WorldCIST ; Conference Date: 11 April 2017 Through 13 April 2017; Conference Code:190479},
keywords = {Clinical information; Decision support system (DSSs); Health care information system; Intelligent multi agent systems; Monitoring system; Multiple data sources; Prevention systems; Progressive expansion, Monitoring},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Carvalhosa2017391,
title = {Pervasiveness in digital marketing – A global overview},
author = {P. Carvalhosa and F. Portela and M. F. Santos and A. Abelha and J. Machado},
editor = {Reis L. P. Rocha A. Costanzo S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018511929&doi=10.1007%2f978-3-319-56541-5_40&partnerID=40&md5=7c927f84b14698028b1489ffcefe8088},
doi = {10.1007/978-3-319-56541-5_40},
issn = {21945357},
year  = {2017},
date = {2017-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {571},
pages = {391-398},
publisher = {Springer Verlag},
abstract = {The adoption of digital marketing techniques, along with eGov tools, allows to create value to the organization and increase the proximity with customers. After selecting a good practice, it should be optimized for the target client, divulgated on the right platforms, implemented, and finally analyzed throughout the implementation process of a good practice. The platforms present in the paper were selected for the analysis, divulgation and optimization of the information of a municipality. The municipalities have problems related to the monetary funds, reason why, they were surveyed free and paid platforms (market leaders). Most of the tools presented are based in pervasiveness concepts as is mobile, available anywhere and anytime and ubiquity. © Springer International Publishing AG 2017.},
note = {cited By 7; Conference of 5th World Conference on Information Systems and Technologies, WorldCIST ; Conference Date: 11 April 2017 Through 13 April 2017; Conference Code:190479},
keywords = {Commerce, Digital marketing; Divulgation and analysis; EGov; Good practices; Implementation process; Market leader, Information systems; Marketing; Optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva2017153,
title = {Text mining models to predict brain deaths using X-Rays clinical notes},
author = {A. Silva and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Gelbukh A. Prasath R.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018394209&doi=10.1007%2f978-3-319-58130-9_15&partnerID=40&md5=211a0c8c61a17d7a26286f64f21c5dbe},
doi = {10.1007/978-3-319-58130-9_15},
issn = {03029743},
year  = {2017},
date = {2017-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {10089 LNAI},
pages = {153-163},
publisher = {Springer Verlag},
abstract = {The prediction of events is a task associated to the Data Science area. In the health, this method is extremely useful to predict critical events that may occur in people, or in a specific area. The Text Mining is a technique that consists in retrieving information from text files. In the Medical Field, the Data Mining and Text Mining solutions can help to prevent the occurrence of certain events to a patient. This project involves the use of Text Mining to predict the Brain Death by using the X-Ray clinical notes. This project is creating reliable predictive models with non-structured text. This project was developed using real data provided by Centro Hospitalar do Porto. The results achieved are very good reaching a sensitivity of 98% and a specificity of 88%. © 2017, Springer International Publishing AG.},
note = {cited By 2; Conference of 4th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2016 ; Conference Date: 13 November 2016 Through 19 November 2016; Conference Code:191459},
keywords = {Brain death; Clinical notes; Critical events; Medical fields; Predictive models; Specific areas; Structured text; Text mining, Data mining, Forecasting; Medical computing; X rays},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Morgado2017433,
title = {A case-based approach to colorectal cancer detection},
author = {P. Morgado and H. Vicente and A. Abelha and J. Machado and J. Neves and J. Neves},
editor = {Joukov N. Kim K.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016116954&doi=10.1007%2f978-981-10-4154-9_50&partnerID=40&md5=cd3473ba9d78ff27021ae76a22930f53},
doi = {10.1007/978-981-10-4154-9_50},
issn = {18761100},
year  = {2017},
date = {2017-01-01},
journal = {Lecture Notes in Electrical Engineering},
volume = {424},
pages = {433-442},
publisher = {Springer Verlag},
abstract = {Colorectal cancer is one of the most common malignancies in developed countries. Although it is not well known what causes this type of cancer, studies have showed that there are certain risk factors associated that may increase the likelihood of developing such malignancy. These factors comprise, among others, individual’s age, lifestyle habits, personal disease history, and genetic syndromes. Despite its high mortality, colorectal cancer may be prevented with an early diagnosis. Thus, this work aims at the development of Artificial Intelligence based decision support system to assess the risk of developing colorectal cancer. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case-based approach to computing that caters for the handling of incomplete, unknown, or even self-contradictory data, information or knowledge. © Springer Nature Singapore Pte Ltd. 2017.},
note = {cited By 2; Conference of 8th International Conference on Information Science and Applications, ICISA 2017 ; Conference Date: 20 March 2017 Through 23 March 2017; Conference Code:190019},
keywords = {Artificial intelligence; Case based reasoning; Computation theory; Computer circuits; Decision support systems; Diagnosis; Genetic programming; Knowledge representation; Logic programming; Risk assessment, Case-based approach; Colorectal cancer; Developed countries; Early diagnosis; Knowledge representation and reasoning; Risk factors, Diseases},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Esteves2017274,
title = {A case based methodology for problem solving aiming at knee osteoarthritis detection},
author = {M. Esteves and H. Vicente and J. Machado and V. Alves and J. Neves},
editor = {Deris M. M. Nawi N.M. Ghazali R.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009818557&doi=10.1007%2f978-3-319-51281-5_28&partnerID=40&md5=95f00ae4942f7bfa66438104f16f7c1a},
doi = {10.1007/978-3-319-51281-5_28},
issn = {21945357},
year  = {2017},
date = {2017-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {549 AISC},
pages = {274-284},
publisher = {Springer Verlag},
abstract = {Knee osteoarthritis is the most common type of arthritis and a major cause of impaired mobility and disability for the ageing populations. Therefore, due to the increasing prevalence of the malady, it is expected that clinical and scientific practices had to be set in order to detect the problem in its early stages. Thus, this work will be focused on the improvement of methodologies for problem solving aiming at the development of Artificial Intelligence based decision support system to detect knee osteoarthritis. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing that caters for the handling of incomplete, unknown, or even self-contradictory information. © Springer International Publishing AG 2017.},
note = {cited By 1; Conference of The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016 ; Conference Date: 18 August 2016 Through 20 August 2016; Conference Code:188139},
keywords = {Ageing population; Case based; Case-based approach; Knee osteoarthritis; Knowledge representation and reasoning; X-ray image, Artificial intelligence; Case based reasoning; Computation theory; Computer circuits; Data mining; Decision support systems; Feature extraction; Knowledge representation; Logic programming; Soft computing, Problem solving},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Machado2016xv,
title = {Intelligent Systems and Applications in Healthcare (ISAHealth 2016 ) Preface},
author = {J. Machado and A. Abelha and M. F. Santos and F. Portela},
editor = {Huemer C. Poels G. Kornyshova E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010382812&doi=10.1109%2fCBI.2016.66&partnerID=40&md5=5633f822b6eca19756886bfa7da0a56f},
doi = {10.1109/CBI.2016.66},
isbn = {9781509032310},
year  = {2016},
date = {2016-01-01},
journal = {Proceedings - CBI 2016: 18th IEEE Conference on Business Informatics},
volume = {2},
pages = {xv-xvi},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
note = {cited By 0; Conference of 18th IEEE Conference on Business Informatics, CBI 2016 ; Conference Date: 29 August 2016 Through 1 September 2016; Conference Code:125364},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva201645,
title = {Towards of automatically detecting brain death patterns through text mining},
author = {A. Silva and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Huemer C. Poels G. Kornyshova E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010380045&doi=10.1109%2fCBI.2016.49&partnerID=40&md5=c823dfb35568399d22d13af6c9e76461},
doi = {10.1109/CBI.2016.49},
isbn = {9781509032310},
year  = {2016},
date = {2016-01-01},
journal = {Proceedings - CBI 2016: 18th IEEE Conference on Business Informatics},
volume = {2},
pages = {45-52},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In the area of medicine, x-rays are very useful to check if the patient suffers from brain death. Their diagnosis is made using free text. This type of record difficult the process of making qualitative analysis in order to automatically detect possible brain problems. This project aims to make qualitatively and quantitatively analysis of Brain Computed Tomography (CT) diagnosis using text analysis tools as is Natural Language Processing and Text Mining. In this work a set of related words that can means patterns in CT reports was detected. The dataset was provided by the Centro Hospitalar do Porto-Hospital de Santo António and it contains information about patient deaths and CT done to the brain. With the analysis made, a new research and analysis perspectives of structured and unstructured texts in this field was opened. © 2016 IEEE.},
note = {cited By 2; Conference of 18th IEEE Conference on Business Informatics, CBI 2016 ; Conference Date: 29 August 2016 Through 1 September 2016; Conference Code:125364},
keywords = {Brain death; NAtural language processing; Qualitative analysis; Related word; Research and analysis; Text analysis; Text mining; Unstructured texts, Computerized tomography; Diagnosis; Information science; Natural language processing systems; X rays, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ribeiro201638,
title = {Patients' admissions in intensive care units: A clustering overview},
author = {A. Ribeiro and F. Portela and M. F. Santos and J. Machado and A. Abelha and F. Rua},
editor = {Huemer C. Poels G. Kornyshova E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010284769&doi=10.1109%2fCBI.2016.48&partnerID=40&md5=0578138e0e8a81352aed1d0cd29a6ccf},
doi = {10.1109/CBI.2016.48},
isbn = {9781509032310},
year  = {2016},
date = {2016-01-01},
journal = {Proceedings - CBI 2016: 18th IEEE Conference on Business Informatics},
volume = {2},
pages = {38-44},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Intensive Care is one of the most critical areas ofmedicine. Its multidisciplinary nature makes it a very wide area, requiring all types of healthcare professionals. Given the criticalenvironment of intensive care units, it becomes evident the need touse technology of decision support systems to improve healthcareservices and Intensive Care Units management. By discovering thecommon characteristics of the admitted patients it is possible toimprove these outcomes. In this study clustering techniques wereapplied to data collected from admitted patients in Intensive CareUnit. The best results presented a Silhouette of 1, with a distance tocentroids of 6.2e-17 and a Davies-Bouldin index of -0.652. © 2016 IEEE.},
note = {cited By 0; Conference of 18th IEEE Conference on Business Informatics, CBI 2016 ; Conference Date: 29 August 2016 Through 1 September 2016; Conference Code:125364},
keywords = {admissions; clustering; Clustering techniques; Davies-Bouldin index; Health care professionals; INTCare; Intensive care, Artificial intelligence; Data mining; Information science; Intensive care units, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@book{Machado2016xvii,
title = {Preface},
author = {J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981268339&doi=10.4018%2f978-1-4666-9882-6&partnerID=40&md5=0385f9994e86fc3d56934bddbbc24537},
doi = {10.4018/978-1-4666-9882-6},
isbn = {9781466698833; 1466698829; 9781466698826},
year  = {2016},
date = {2016-01-01},
journal = {Applying Business Intelligence to Clinical and Healthcare Organizations},
pages = {xvii-xxv},
publisher = {IGI Global},
note = {cited By 2},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
@book{Machado20161,
title = {Applying business intelligence to clinical and healthcare organizations},
author = {J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981266975&doi=10.4018%2f978-1-4666-9882-6&partnerID=40&md5=a51ba7f282df14d7cdc97f90431b864e},
doi = {10.4018/978-1-4666-9882-6},
isbn = {9781466698833; 1466698829; 9781466698826},
year  = {2016},
date = {2016-01-01},
journal = {Applying Business Intelligence to Clinical and Healthcare Organizations},
pages = {1-347},
publisher = {IGI Global},
abstract = {Business intelligence (BI) tools are capable of working with healthcare data in an efficient manner to generate real-time information and knowledge relevant to the success of healthcare organizations. Further, BI tools benefit healthcare professionals making critical decisions within hospitals, clinics, and physicians' offices. Applying Business Intelligence to Clinical and Healthcare Organizations presents new solutions for data analysis within the healthcare sector in order to improve the quality of medical care and patient quality of life. Business intelligence models and techniques are explored and their benefits for the healthcare sector exposed in this timely research-based publication comprised of chapters written by professionals and researchers from around the world. Hospital administrators, healthcare professionals, biomedical engineers, informatics engineers, and students in graduate-level healthcare management programs will find this publication essential to their professional development and research needs. © 2016 by IGI Global. All rights reserved.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
@inbook{Machado2016256,
title = {Applying soft computing to clinical decision support},
author = {J. Machado and L. Oliveira and L. Barreiro and S. Pinto and A. Coimbra},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981237614&doi=10.4018%2f978-1-4666-9882-6.ch013&partnerID=40&md5=583afdfe187559b1e73801cd3a0a38cb},
doi = {10.4018/978-1-4666-9882-6.ch013},
isbn = {9781466698833; 1466698829; 9781466698826},
year  = {2016},
date = {2016-01-01},
journal = {Applying Business Intelligence to Clinical and Healthcare Organizations},
pages = {256-271},
publisher = {IGI Global},
abstract = {This article aims to explain the construction process of the learing systems based on Artificial Neural Networks and Genetic Algorithms. These systems were implemented using R and Python programming languages, in order to compare results and achieve the best solution and it was used Diabetes and Parkinson datasets with the purpose of identifying the carriers of these diseases. © 2016, IGI Global. All rights reserved.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Gonçalves2016241,
title = {Clinical business intelligence to prevent stroke accidents},
author = {N. Gonçalves and C. Quintas and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981203308&doi=10.4018%2f978-1-4666-9882-6.ch012&partnerID=40&md5=543978bd2340c512e54d3dd3b137c9ed},
doi = {10.4018/978-1-4666-9882-6.ch012},
isbn = {9781466698833; 1466698829; 9781466698826},
year  = {2016},
date = {2016-01-01},
journal = {Applying Business Intelligence to Clinical and Healthcare Organizations},
pages = {241-255},
publisher = {IGI Global},
abstract = {Stroke is considered the third main cause of death among all population, without distinguishing genders, led by heart diseases in first place. In other hand, despite representing a significant number of mortality, these diseases are the causes for a long-term disability in all countries with a vast recovery time going parallel with its costs. However, leaving aside this facts, stokes and heath diseases can also be easily prevented considering the outcome. This paper presents a new methodology to prevent these events to happen by using segmentation methods, which allows distinguishing and aggregating clusters of historical records, classification methods, such as Artificial Neural Networks, capable of classifying a new record according to its distribution among the clusters. A Multi-Agent Case Based Reasoning system is also proposed to evaluate solutions based in a similar case. © 2016, IGI Global. All rights reserved.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@article{Oliveira2016183,
title = {Clustering data mining models to identify patterns in weaning patient failures},
author = {S. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040247764&partnerID=40&md5=f6f7a070b9b3166edefc3b32424a0693},
issn = {19984510},
year  = {2016},
date = {2016-01-01},
journal = {International Journal of Biology and Biomedical Engineering},
volume = {10},
pages = {183-190},
publisher = {North Atlantic University Union NAUN},
abstract = {The weaning process of ventilated patients need to be carefully performed. This type of procedure is very common in Intensive Medicine. The procedure is well-defined and it is executed according the patient condition. During the weaning process, the patient can be in vary stages. At the end the extubation tentative can be considered as successful or not. Before the extubation, the patient is submitted to a set of tests in order to validate the procedure. When this procedure is wrong executed, it can provoke long term injuries to the patient. This work arises in order to avoid weaning failures by early detecting the procedure result. This work has as main goal identify possible patient patterns associated to weaning failures. In this context Clustering data mining was used to select and identify the features and the patterns associated to failures. As result an Index-Davies Bouldin of 0.51 was achieved and the most significant variables associated to a failure were identified. The physicians has now new and useful knowledge able to help to take a decision about weaning before it be initiated. © 2016 North Atlantic University Union NAUN. All rights reserved.},
note = {cited By 3},
keywords = {Article; artificial ventilation; data analysis; data mining; extubation; human; intensive care unit; ventilated patient; ventilator weaning},
pubstate = {published},
tppubtype = {article}
}
@article{Esteves2016296,
title = {Waiting time screening in diagnostic medical imaging – A case-based view},
author = {M. Esteves and H. Vicente and S. Gomes and A. Abelha and M. F. Santos and J. Machado and J. Neves and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007569805&doi=10.1007%2f978-3-319-40973-3_30&partnerID=40&md5=55888f4bc680dfd31d74ab83d00f1b57},
doi = {10.1007/978-3-319-40973-3_30},
issn = {03029743},
year  = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9714 LNCS},
pages = {296-308},
publisher = {Springer Verlag},
abstract = {Due to the high standards expected from diagnostic medical imaging, the analysis of information regarding waiting lists via different information systems is of utmost importance. Such analysis, on the one hand, may improve the diagnostic quality and, on the other hand, may lead to the reduction of waiting times, with the concomitant increase of the quality of services and the reduction of the inherent financial costs. Hence, the purpose of this study is to assess the waiting time in the delivery of diagnostic medical imaging services, like computed tomography and magnetic resonance imaging. Thereby, this work is focused on the development of a decision support system to assess waiting times in diagnostic medical imaging with recourse to operational data of selected attributes extracted from distinct information systems. The computational framework is built on top of a Logic Programming Case-base Reasoning approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information. © Springer International Publishing Switzerland 2016.},
note = {cited By 4},
keywords = {Case-base reasonings; Computational framework; Diagnostic quality; Imaging services; Knowledge representation and reasoning; Operational data; Similarity analysis; Waiting-time, Medical imaging},
pubstate = {published},
tppubtype = {article}
}
@article{Brandão2016,
title = {A benchmarking analysis of open-source business intelligence tools in healthcare environments},
author = {A. Brandão and E. Pereira and M. Esteves and F. Portela and M. F. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007290342&doi=10.3390%2finfo7040057&partnerID=40&md5=fadb3f55df718631467887870b3ba1af},
doi = {10.3390/info7040057},
issn = {20782489},
year  = {2016},
date = {2016-01-01},
journal = {Information (Switzerland)},
volume = {7},
number = {4},
publisher = {MDPI AG},
abstract = {In recent years, a wide range of Business Intelligence (BI) technologies have been applied to different areas in order to support the decision-making process. BI enables the extraction of knowledge from the data stored. The healthcare industry is no exception, and so BI applications have been under investigation across multiple units of different institutions. Thus, in this article, we intend to analyze some open-source/free BI tools on the market and their applicability in the clinical sphere, taking into consideration the general characteristics of the clinical environment. For this purpose, six BI tools were selected, analyzed, and tested in a practical environment. Then, a comparison metric and a ranking were defined for the tested applications in order to choose the one that best applies to the extraction of useful knowledge and clinical data in a healthcare environment. Finally, a pervasive BI platform was developed using a real case in order to prove the tool viability. © 2016 by the authors.},
note = {cited By 31},
keywords = {Benchmarking; Competitive intelligence; Decision making; Extraction; Information analysis, Clinical data; Clinical environments; Decision making process; Healthcare environments; Healthcare industry; Open sources; Real case, Health care},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Coelho2016762,
title = {Towards of a Business Intelligence Platform to Portuguese Misericórdias},
author = {D. Coelho and J. Miranda and F. Portela and J. Machado and M. F. Santos and A. Abelha},
editor = {Cruz-Cunha M. M. Rijo R. Martinho R.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006877786&doi=10.1016%2fj.procs.2016.09.222&partnerID=40&md5=0dbe6e7ab4e67743318c6f0a991e55f8},
doi = {10.1016/j.procs.2016.09.222},
issn = {18770509},
year  = {2016},
date = {2016-01-01},
journal = {Procedia Computer Science},
volume = {100},
pages = {762-767},
publisher = {Elsevier B.V.},
abstract = {In the healthcare industry it is imperative the need to increase the efficiency of resource management and services. The increasing of Business Intelligence (BI) use in organizations and the demonstrated effectiveness of this type of solution, arises the desire to use BI in healthcare as in Misericórdias. So, in this work some concepts associated to the use of BI in Misericórdias were addressed and a BI architecture was designed. Furthermore, a survey was made in order to understand what are the tools used by Misericórdias every day and which ones have the BI components. Finally, a BI architecture was developed based on the organization's mission and their stakeholders. Through this work it was possible to identify the critical processes and designing the Entity Relationship Diagram as well as a set of indicators to meet the needs of a sustainable decision-making in Portuguese Misericórdias. © 2016 The Authors.},
note = {cited By 12; Conference of Conference on ENTERprise Information Systems / International Conference on Project MANagement / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / ProjMAN / HCist 2016 ; Conference Date: 5 October 2016 Through 7 October 2016; Conference Code:121650},
keywords = {Business Intelligence platform; Entity relationship diagrams; Healthcare industry; Resource management; Sustainable decision makings, Competitive intelligence; Data warehouses; Decision making; Health care; Information analysis; Information systems; Management science; Project management, Information management},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Foshch2016253,
title = {Regression Models of the Nuclear Power Unit VVER-1000 Using Data Mining Techniques},
author = {T. Foshch and F. Portela and J. Machado and M. Maksimov},
editor = {Cruz-Cunha M. M. Rijo R. Martinho R.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006851408&doi=10.1016%2fj.procs.2016.09.151&partnerID=40&md5=3c09489c8dd1a99e4f8a73d5f0451ac3},
doi = {10.1016/j.procs.2016.09.151},
issn = {18770509},
year  = {2016},
date = {2016-01-01},
journal = {Procedia Computer Science},
volume = {100},
pages = {253-262},
publisher = {Elsevier B.V.},
abstract = {Due to plenty of changes in many interrelated processes at nuclear power plants there is the need to show which values of some parameters of the nuclear power plant with VVER-1000 are better. In this task data mining techniques can be introduced. In order to obtain regression models of nuclear power plant with VVER-1000 algorithms such as the Linear Regression, REPTree, and M5P were selected and the datasets were obtained by simulating two control programs in Simulink software. The study focused on such targets as the average temperature of the coolant in the first circuit and the output power of the power generator. This study demonstrates the good results of the correlation coefficients and the root relative squared error metrics in case of the improved compromise-combined control program in comparison with the control program with the constant average temperature of the coolant in the reactor core. In terms of the results the root relative squared error metric is less than 2.8% and the correlation coefficients had values higher than 99,95%. The use of these models can contribute to improving the understanding of the internal processes because using the best regression data mining models allows to see advantages of the improved compromise-combined control program. © 2016 The Authors.},
note = {cited By 5; Conference of Conference on ENTERprise Information Systems / International Conference on Project MANagement / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / ProjMAN / HCist 2016 ; Conference Date: 5 October 2016 Through 7 October 2016; Conference Code:121650},
keywords = {Combined control; Control program; Correlation coefficient; Data mining models; Nuclear power unit; Regression model; Simulink software; VVER-1000, Coolants; Data mining; Information systems; Nuclear energy; Nuclear fuels; Nuclear power plants; Project management; Regression analysis, Information management},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Peixoto2016528,
title = {Resurgery Clusters in Intensive Medicine},
author = {R. Peixoto and F. Portela and F. Pinto and M. F. Santos and J. Machado and A. Abelha and F. Rua},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992435562&doi=10.1016%2fj.procs.2016.09.072&partnerID=40&md5=c079429a47641fbe04385e2e5678042a},
doi = {10.1016/j.procs.2016.09.072},
issn = {18770509},
year  = {2016},
date = {2016-01-01},
journal = {Procedia Computer Science},
volume = {58},
pages = {528-533},
publisher = {Elsevier B.V.},
abstract = {The field of critical care medicine is confronted every day with cases of surgical interventions. When Data Mining is properly applied in this field, it is possible through predictive models to identify if a patient, should or should not have surgery again upon the same problem. The goal of this work is to apply clustering techniques in collected data in order to categorize re-interventions in intensive care. By knowing the common characteristics of the re-intervention patients it will be possible to help the physician to predict a future resurgery. For this study various attributes were used related to the patient's health problems like heart problems or organ failure. For this study it was also considered important aspects such as age and what type of surgery the patient was submitted. Classes were created with the patients' age and the number of days after the first surgery. Another class was created where the type of surgery that the patient was operated upon was identified. This study comprised Davies Bouldin values between -0.977 and -0.416. The used variables, in addition to being provided by Hospital de Santo António in Porto, they are provided from the electronic medical record. © 2016 The Authors.},
note = {cited By 0; Conference of 7th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2016 / The 6th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH-2016 / Affiliated Workshops, 2016 ; Conference Date: 19 September 2016 Through 22 September 2016; Conference Code:131700},
keywords = {Clustering; Clustering techniques; Critical care medicine; Electronic medical record; INTCare; Intervention; Predictive models; Surgical interventions, Data mining; Health care; Intensive care units; Medical computing; Medicine; Surgery, Patient treatment},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira2016362,
title = {Pervasive Business Intelligence: A New Trend in Critical Healthcare},
author = {A. Pereira and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992378379&doi=10.1016%2fj.procs.2016.09.055&partnerID=40&md5=fbb4dfe04c80c78a495877155c46f2d5},
doi = {10.1016/j.procs.2016.09.055},
issn = {18770509},
year  = {2016},
date = {2016-01-01},
journal = {Procedia Computer Science},
volume = {58},
pages = {362-367},
publisher = {Elsevier B.V.},
abstract = {In the field of intensive medicine, presentation of medical information is identified as a major concern for the health professionals, since it can be a great aid when it is necessary to make decisions, of varying gravity, for the patient's state. The way in which this information is presented, and especially when it is presented, may make it difficult for the intensivists within intense healthcare units to understand a patient's state in a timely fashion. Should there be a need to cross various types of clinical data from various sources, the situation worsens considerably. To support the health professional's decision-making process, the Pervasive Business Intelligence (PBI) Systems are a forthcoming field. Based on this principle, the current study approaches the way to present information about the patients, after they are received in a BI system, making them available at any place and at any time for the intensivists that may need it for the decision-making. The patient's history will, therefore, be available, allowing examination of the vital signs data, what medicine that they might need, health checks performed, among others. Then, it is of vital importance, to make these conclusions available to the health professionals every time they might need, so as to aid them in the decision-making. This study aims to make a stance by approaching the theme of PBI in Critical Healthcare. The main objective is to understand the underlying concepts and the assets of BI solutions with Pervasive characteristics. Perhaps consider it a sort of guide or a path to follow for those who wish to insert Pervasive into Business Intelligence in Healthcare area. © 2016 The Authors.},
note = {cited By 9; Conference of 7th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2016 / The 6th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH-2016 / Affiliated Workshops, 2016 ; Conference Date: 19 September 2016 Through 22 September 2016; Conference Code:131700},
keywords = {Decision making process; Health checks; Health professionals; Medical information; Pervasive; Pervasive healthcare; Pervasive systems; Ubiquitous systems, Decision making; Health; Health care; Information analysis, Ubiquitous computing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Quintas201629,
title = {A case based approach to assess waiting time prediction at an intensive care unity},
author = {A. Quintas and H. Vicente and P. Novais and A. Abelha and M. F. Santos and J. Machado and J. Neves},
editor = {Arezes P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986268659&doi=10.1007%2f978-3-319-41929-9_4&partnerID=40&md5=14537afce594fb1c8883221af2f7b789},
doi = {10.1007/978-3-319-41929-9_4},
issn = {21945357},
year  = {2016},
date = {2016-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {491},
pages = {29-39},
publisher = {Springer Verlag},
abstract = {Waiting time at an intensive care unity stands for a key feature in the assessment of healthcare quality. Nevertheless, its estimation is a difficult task, not only due to the different factors with intricate relations among them, but also with respect to the available data, which may be incomplete, self-contradictory or even unknown. However, its prediction not only improves the patients’ satisfaction but also enhance the quality of the healthcare being provided. To fulfill this goal, this work aims at the development of a decision support system that allows one to predict how long a patient should remain at an emergency unit, having into consideration all the remarks that were just stated above. It is built on top of a Logic Programming approach to knowledge representation and reasoning, complemented with a Case Base approach to computing. © Springer International Publishing Switzerland 2016.},
note = {cited By 10; Conference of International Conference on Safety Management and Human Factors, 2016 ; Conference Date: 27 July 2016 Through 31 July 2016; Conference Code:180619},
keywords = {Artificial intelligence; Computation theory; Computer circuits; Decision support systems; Forecasting; Health care; Human engineering; Knowledge representation; Logic programming; Reconfigurable hardware, Case based reasoning, Case-based approach; Healthcare quality; Intensive care; Key feature; Knowledge representation and reasoning; Similarity analysis; Waiting-time; Waiting-time prediction},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Faria2016159,
title = {A case-based approach to nosocomial infection detection},
author = {R. Faria and H. Vicente and A. Abelha and M. Santos and J. Machado and J. Neves},
editor = {Rutkowski L. Tadeusiewicz R. Zurada J.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977478473&doi=10.1007%2f978-3-319-39384-1_14&partnerID=40&md5=e0e813cd5ebc01c8fb96823371bff0ff},
doi = {10.1007/978-3-319-39384-1_14},
issn = {03029743},
year  = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9693},
pages = {159-168},
publisher = {Springer Verlag},
abstract = {The nosocomial infections are a growing concern because they affect a large number of people and they increase the admission time in healthcare facilities. Additionally, its diagnosis is very tricky, requiring multiple medical exams. So, this work is focused on the development of a clinical decision support system to prevent these events from happening. The proposed solution is unique once it caters for the explicit treatment of incomplete, unknown, or even contradictory information under a logic programming basis, that to our knowledge is something that happens for the first time. © Springer International Publishing Switzerland 2016.},
note = {cited By 2; Conference of 15th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2016 ; Conference Date: 12 June 2016 Through 16 June 2016; Conference Code:177089},
keywords = {Artificial intelligence; Computation theory; Computer circuits; Decision support systems; Diagnosis; Health care; Knowledge representation; Logic programming; Reconfigurable hardware; Soft computing, Case based reasoning, Case-based approach; Clinical decision support systems; Explicit treatments; Healthcare facility; Knowledge representation and reasoning; Nosocomial infection; Number of peoples; Similarity analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Coimbra2016115,
title = {Prediction of length of hospital stay in preterm infants a case-based reasoning view},
author = {A. Coimbra and H. Vicente and A. Abelha and M. Filipe Santos and J. Machado and J. Neves and J. Neves},
editor = {Czarnowski I. Howlett R.J. Jain L.C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977097691&doi=10.1007%2f978-3-319-39630-9_10&partnerID=40&md5=6289feafb914ab92696ef065ab6419ed},
doi = {10.1007/978-3-319-39630-9_10},
issn = {21903018},
year  = {2016},
date = {2016-01-01},
journal = {Smart Innovation, Systems and Technologies},
volume = {56},
pages = {115-128},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The length of stay of preterm infants in a neonatology service has become an issue of a growing concern, namely considering, on the one hand, the mothers and infants health conditions and, on the other hand, the scarce healthcare facilities own resources. Thus, a pro-active strategy for problem solving has to be put in place, either to improve the quality-of-service provided or to reduce the inherent financial costs. Therefore, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a case-based problem solving methodology to computing, that caters for the handling of incomplete, unknown, or even contradictory information. The proposed model has been quite accurate in predicting the length of stay (overall accuracy of 84.9 %) and by reducing the computational time with values around 21.3 %. © Springer International Publishing Switzerland 2016.},
note = {cited By 1; Conference of 8th KES International Conference on Intelligent Decision Technologies, KES-IDT 2016 ; Conference Date: 15 June 2016 Through 17 June 2016; Conference Code:177269},
keywords = {Artificial intelligence; Case based reasoning; Computation theory; Computer circuits; Decision support systems; Knowledge representation; Logic programming; Program diagnostics; Quality of service; Reconfigurable hardware, Diagnosis decision; Healthcare facility; Knowledge representation and reasoning; Length of hospital stays; Length of stay; Neonatology; Overall accuracies; Preterm infants, Problem solving},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neves2016273,
title = {Length of hospital stay and quality of care},
author = {J. Neves and V. Abelha and H. Vicente and J. Neves and J. Machado},
editor = {Kacprzyk J. Skulimowski A.M.J. Papadopoulos G.A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975720683&doi=10.1007%2f978-3-319-27478-2_19&partnerID=40&md5=e94d0fa5c4bd31c39abe6b1aef60148b},
doi = {10.1007/978-3-319-27478-2_19},
issn = {21945357},
year  = {2016},
date = {2016-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {416},
pages = {273-287},
publisher = {Springer Verlag},
abstract = {The relationship between Length Of hospital Stay (LOS) and Quality-of- Care (QofC) is demanding and difficult to assess. Indeed, a multifaceted intertwining network of countless services and LOS factors is available, which may range from organizational culture to hospital physicians availability, without discarding the possibility of lifting the foot on intermediate care services, to the customs and cultures of the people. On health policy terms, LOS remains a measurable index of efficiency, and most of the studies that have been undertaken show that QoC or health outcomes do not appear to be compromised by reductions in LOS times. Therefore, and in order to assess this statement, a Logic Programming based methodology to Knowledge Representation and Reasoning, allowing the modeling of the universe of discourse in terms of defective data, information and knowledge is used, being complemented with an Artificial Neural Networks based approach to computing, allowing one to predict for how long a patient should remain in a hospital or at home, during his/her illness experience. © Springer International Publishing Switzerland 2016.},
note = {cited By 3; Conference of 9th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2014 ; Conference Date: 6 November 2014 Through 8 November 2014; Conference Code:164129},
keywords = {Computation theory; Computer circuits; Knowledge representation; Logic programming; Neural networks; Reconfigurable hardware, Health outcomes; Health policy; Intermediate cares; Knowledge representation and reasoning; Length of hospital stays; Organizational cultures; Quality of care; Universe of discourse, Hospitals},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Braga20161,
title = {Data mining to predict the use of vasopressors in intensive medicine patients},
author = {A. Braga and F. Portela and M. F. Santos and A. Abelha and J. Machado and Á. Silva and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975029552&doi=10.11113%2fjt.v78.9075&partnerID=40&md5=c205b845031b35fe953ab55e8d8ef5b7},
doi = {10.11113/jt.v78.9075},
issn = {01279696},
year  = {2016},
date = {2016-01-01},
journal = {Jurnal Teknologi},
volume = {78},
number = {6-7},
pages = {1-6},
publisher = {Penerbit UTM Press},
abstract = {The role that decision making process plays in Intensive Medicine is very critical essential due to the bad health condition of the patients that go to Intensive Care Units (ICU) and the need of a quick and accurate decisions. Therefore each decision is crucial, because it can help saving endangered lives. The decision should be always taken in the patient best interest after analyzing all the data available. In the eyes of the intensivists, the ever growing amount of available data concerning the patients, makes it each time more difficult for them to make a decision based on so many information. It is based on this ideal of improving the decision making process, that this work arises and Data Mining models were induced to predict if a patient will need to take a vasopressor, more specifically: Dopamine, Adrenaline or Noradrenaline. This work used real data provided by an Intensive Care Unit and collected in real-time. The data mining model were induced using data from vital sign monitors, laboratory analysis and information about the patient’s Electronic Health Record. This study was based in clinical evidences and provided very useful results with a sensitivity around 90%. These models will reduce the need of vasopressor drugs by helping intensivists to act and take accurate decision before the vasopressor be need by the patient. It will improve the patient condition because when the time comes the predicted necessity of the vasopressor will cease to exist due to the early care provided by the intensivist. The decisions can be for example change the therapeutic plan. Overall, the decision making process becomes more reliable and effective and the quality of care given to patients is better. © 2016 Penerbit UTM Press. All rights reserved.},
note = {cited By 7},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Neves20163,
title = {Screening a case base for stroke disease detection},
author = {J. Neves and N. Gonçalves and R. Oliveira and S. Gomes and J. Neves and J. Macedo and A. Abelha and C. Analide and J. Machado and M. F. Santos and H. Vicente},
editor = {Quintian H. Troncoso A. Martinez-Alvarez F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964056771&doi=10.1007%2f978-3-319-32034-2_1&partnerID=40&md5=8b11d528fb7c9db682be64a47fb6730e},
doi = {10.1007/978-3-319-32034-2_1},
issn = {03029743},
year  = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9648},
pages = {3-13},
publisher = {Springer Verlag},
abstract = {Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality figure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases retrieval one. On the other hand, and aiming at an improvement of the CBR theoretical basis, the predicates` attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were rewritten, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of oneʼs confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown. © Springer International Publishing Switzerland 2016.},
note = {cited By 2; Conference of 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016 ; Conference Date: 8 April 2016 Through 20 April 2016; Conference Code:173819},
keywords = {Artificial intelligence; Computation theory; Computer circuits; Decision support systems; Diagnosis; Intelligent systems; Knowledge representation; Logic programming; Quality control; Reconfigurable hardware; Risk assessment, Case based reasoning, Case-based reasoning approaches; Clustering methods; Degree of confidence; Disease detection; Knowledge representation and reasoning; Quality of informations (QoI); Similarity analysis; Universe of discourse},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2016599,
title = {Optimization techniques to detect early ventilation extubation in intensive care units},
author = {P. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},
editor = {Reis L. P. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961661729&doi=10.1007%2f978-3-319-31307-8_62&partnerID=40&md5=6905a09f181105aa643287014acb6a94},
doi = {10.1007/978-3-319-31307-8_62},
issn = {21945357},
year  = {2016},
date = {2016-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {445},
pages = {599-608},
publisher = {Springer Verlag},
abstract = {The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment. © Springer International Publishing Switzerland 2016.},
note = {cited By 2; Conference of World Conference on Information Systems and Technologies, WorldCIST 2016 ; Conference Date: 22 March 2016 Through 24 March 2016; Conference Code:172089},
keywords = {Artificial intelligence; Decision making; Decision support systems; Decision trees; Forestry; Fuzzy inference; Genetic algorithms; Information systems; Intensive care units; Learning systems; Trees (mathematics), Decision making process; Decision support models; Fuzzy rule learning; Heuristics; Optimization techniques; Real environments; Research and development; Supervised classifiers, Optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira2016579,
title = {Predicting triage waiting time in maternity emergency care by means of data mining},
author = {S. Pereira and L. Torres and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Reis L. P. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961639111&doi=10.1007%2f978-3-319-31307-8_60&partnerID=40&md5=f3949e7b9e5bc5b1211b23e76d69bde2},
doi = {10.1007/978-3-319-31307-8_60},
issn = {21945357},
year  = {2016},
date = {2016-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {445},
pages = {579-588},
publisher = {Springer Verlag},
abstract = {Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times. © Springer International Publishing Switzerland 2016.},
note = {cited By 5; Conference of World Conference on Information Systems and Technologies, WorldCIST 2016 ; Conference Date: 22 March 2016 Through 24 March 2016; Conference Code:172089},
keywords = {Adverse events; Emergency care; Healthcare organizations; Prediction model; Waiting-time, Artificial intelligence; Decision support systems; Embedded systems; Forecasting; Information systems, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2016567,
title = {Pervasive adaptive data acquisition gateway for critical healthcare},
author = {S. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Reis L. P. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961625750&doi=10.1007%2f978-3-319-31307-8_59&partnerID=40&md5=4ad2c581e2e4bd82b46a0a6922dfd897},
doi = {10.1007/978-3-319-31307-8_59},
issn = {21945357},
year  = {2016},
date = {2016-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {445},
pages = {567-576},
publisher = {Springer Verlag},
abstract = {The data acquisition process in real-time is fundamental to provide appropriate services and improve health professionals decision. In this paper a pervasive adaptive data acquisition architecture of medical devices (e.g. vital signs, ventilators and sensors) is presented. The architecture was deployed in a real context in an Intensive Care Unit. It is providing clinical data in real-time to the INTCare system. The gateway is composed by several agents able to collect a set of patients’ variables (vital signs, ventilation) across the network. The paper shows as example the ventilation acquisition process. The clients are installed in a machine near the patient bed. Then they are connected to the ventilators and the data monitored is sent to a multithreading server which using Health Level Seven protocols records the data in the database. The agents associated to gateway are able to collect, analyse, interpret and store the data in the repository. This gateway is composed by a fault tolerant system that ensures a data store in the database even if the agents are disconnected. The gateway is pervasive, universal, and interoperable and it is able to adapt to any service using streaming data. © Springer International Publishing Switzerland 2016.},
note = {cited By 1; Conference of World Conference on Information Systems and Technologies, WorldCIST 2016 ; Conference Date: 22 March 2016 Through 24 March 2016; Conference Code:172089},
keywords = {Adaptability; Data streaming; Medical Devices; Pervasive Data; Real time; Vital sign, Biomedical equipment; Data acquisition; Data handling; Data processing; Information systems; Intensive care units; Interoperability; Network architecture; Reconfigurable hardware; Sensors; Ventilation, Gateways (computer networks)},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela2016589,
title = {Critical events in mechanically ventilated patients},
author = {F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},
editor = {Reis L. P. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961621859&doi=10.1007%2f978-3-319-31307-8_61&partnerID=40&md5=a19b2c10489ffc9a738572de04a0b9e5},
doi = {10.1007/978-3-319-31307-8_61},
issn = {21945357},
year  = {2016},
date = {2016-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {445},
pages = {589-598},
publisher = {Springer Verlag},
abstract = {Mechanical Ventilation is an artificial way to help a Patient to breathe. This procedure is used to support patients with respiratory diseases however in many cases it can provoke lung damages, Acute Respiratory Diseases or organ failure. With the goal to early detect possible patient breath problems a set of limit values was defined to some variables monitored by the ventilator (Average Ventilation Pressure, Compliance Dynamic, Flow, Peak, Plateau and Support Pressure, Positive end-expiratory pressure, Respiratory Rate) in order to create critical events. A critical event is verified when a patient has a value higher or lower than the normal range defined for a certain period of time. The values were defined after elaborate a literature review and meeting with physicians specialized in the area. This work uses data streaming and intelligent agents to process the values collected in real-time and classify them as critical or not. Real data provided by an Intensive Care Unit were used to design and test the solution. In this study it was possible to understand the importance of introduce critical events for Mechanically Ventilated Patients. In some cases a value is considered critical (can trigger an alarm) however it is a single event (instantaneous) and it has not a clinical significance for the patient. The introduction of critical events which crosses a range of values and a predefined duration contributes to improve the decision-making process by decreasing the number of false positives and having a better comprehension of the patient condition. © Springer International Publishing Switzerland 2016.},
note = {cited By 2; Conference of World Conference on Information Systems and Technologies, WorldCIST 2016 ; Conference Date: 22 March 2016 Through 24 March 2016; Conference Code:172089},
keywords = {Critical events; INTCare; Intensive care; Real time; Streaming data; Ventilated Patients, Data acquisition; Decision making; Intelligent agents; Intensive care units; Interoperability; Pulmonary diseases; Ventilation, Information systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Braga2016527,
title = {Pervasive patient timeline for intensive care units},
author = {A. Braga and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},
editor = {Reis L. P. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961613566&doi=10.1007%2f978-3-319-31307-8_55&partnerID=40&md5=c888db54cdfaeb5dc704f1a54eed942f},
doi = {10.1007/978-3-319-31307-8_55},
issn = {21945357},
year  = {2016},
date = {2016-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {445},
pages = {527-536},
publisher = {Springer Verlag},
abstract = {This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts. © Springer International Publishing Switzerland 2016.},
note = {cited By 4; Conference of World Conference on Information Systems and Technologies, WorldCIST 2016 ; Conference Date: 22 March 2016 Through 24 March 2016; Conference Code:172089},
keywords = {Clinical information; Critical care; Decision process; INTCare; Patient-centred; Pervasive Patient Timeline; Timeline; Vital sign, Data mining; Information systems, Intensive care units},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Braga201615,
title = {Real-time models to predict the use of vasopressors in monitored patients},
author = {A. Braga and F. Portela and M. F. Santos and A. Abelha and J. Machado and Á. Silva and F. Rua},
editor = {Zheng X. Zeng D.D. Chen H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958580639&doi=10.1007%2f978-3-319-29175-8_2&partnerID=40&md5=4cd41eed311b3d67d7a3dfef7fa8c2d3},
doi = {10.1007/978-3-319-29175-8_2},
issn = {03029743},
year  = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9545},
pages = {15-25},
publisher = {Springer Verlag},
abstract = {The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors. © Springer International Publishing Switzerland 2016.},
note = {cited By 0; Conference of International Conference for Smart Health, ICSH 2015 ; Conference Date: 17 November 2015 Through 18 November 2015; Conference Code:163109},
keywords = {Data mining, Data mining models; Decision making process; INTCare; Laboratory analysis; Real time; Real-time data mining; Vasopressors; Vital sign, Decision making; Patient treatment},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira2016105,
title = {Predicting pre-triage waiting time in a maternity emergency room through data mining},
author = {S. Pereira and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Zheng X. Zeng D.D. Chen H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958528894&doi=10.1007%2f978-3-319-29175-8_10&partnerID=40&md5=2950ba905955e8200d582703152d613b},
doi = {10.1007/978-3-319-29175-8_10},
issn = {03029743},
year  = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9545},
pages = {105-117},
publisher = {Springer Verlag},
abstract = {An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The implementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services. © Springer International Publishing Switzerland 2016.},
note = {cited By 5; Conference of International Conference for Smart Health, ICSH 2015 ; Conference Date: 17 November 2015 Through 18 November 2015; Conference Code:163109},
keywords = {Adverse events; Business Intelligence platform; Classification algorithm; Emergency care; IDSS; Information systems and technologies; Maternity care; Triage system, Artificial intelligence; Decision support systems; Emergency rooms; Forecasting; Gynecology; Health; Interoperability; Obstetrics, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Neves2015,
title = {A Soft Computing Approach to Kidney Diseases Evaluation},
author = {J. Neves and M. R. Martins and J. Vilhena and J. Neves and S. Gomes and A. Abelha and J. Machado and H. Vicente},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940103338&doi=10.1007%2fs10916-015-0313-4&partnerID=40&md5=994dce86d53ced30051b25a6ef3c934e},
doi = {10.1007/s10916-015-0313-4},
issn = {01485598},
year  = {2015},
date = {2015-01-01},
journal = {Journal of Medical Systems},
volume = {39},
number = {10},
publisher = {Springer New York LLC},
abstract = {Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively. © 2015, Springer Science+Business Media New York.},
note = {cited By 27},
keywords = {Acute Disease; Chronic Disease; Comorbidity; Decision Support Techniques; Diagnosis, biological marker, Differential; Female; Health Behavior; Humans; Kidney Diseases; Kidney Function Tests; Male; Middle Aged; Neural Networks (Computer); Reproducibility of Results; Risk Factors},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Fernandes2015362,
title = {Artificial neural networks in diabetes control},
author = {F. Fernandes and H. Vicente and A. Abelha and J. Machado and P. Novais and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957802910&doi=10.1109%2fSAI.2015.7237169&partnerID=40&md5=0dc04aa71077b0100cbb96cd215d2473},
doi = {10.1109/SAI.2015.7237169},
isbn = {9781479985470},
year  = {2015},
date = {2015-01-01},
journal = {Proceedings of the 2015 Science and Information Conference, SAI 2015},
pages = {362-370},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Diabetes Mellitus is now a prevalent disease in both developed and underdeveloped countries, being a major cause of morbidity and mortality. Overweight/obesity and hypertension are potentially modifiable risk factors for diabetes mellitus, and persist during the course of the disease. Despite the evidence from large controlled trials establishing the benefit of intensive diabetes management in reducing microvasculars and macrovasculars complications, high proportions of patients remain poorly controlled. Poor and inadequate glycemic control among patients with Type 2 diabetes constitutes a major public health problem and a risk factor for the development of diabetes complications. In clinical practice, optimal glycemic control is difficult to obtain on a long-term basis, once the reasons for feebly glycemic control are complex. Therefore, this work will focus on the development of a diagnosis support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centred on Artificial Neural Networks, to evaluate the Diabetes states and the Degree-of-Confidence that one has on such a happening. © 2015 IEEE.},
note = {cited By 53; Conference of Science and Information Conference, SAI 2015 ; Conference Date: 28 July 2015 Through 30 July 2015; Conference Code:117981},
keywords = {Clinical practices; Degree of confidence; Diabetes management; Diabetes mellitus; Diagnosis support systems; Formal framework; Knowledge representation and reasoning; Quality of information, Complex networks; Computation theory; Diagnosis; Health risks; Knowledge representation; Logic programming; Neural networks; Program diagnostics; Reconfigurable hardware, Computer circuits},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira201598,
title = {Clustering-based approach for categorizing pregnant women in obstetrics and maternity care},
author = {S. Pereira and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Desai B. C. Toyoma M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955616611&doi=10.1145%2f2790798.2790814&partnerID=40&md5=f331c62252e6594ad3edda41d3fa8ba6},
doi = {10.1145/2790798.2790814},
isbn = {9781450334198},
year  = {2015},
date = {2015-01-01},
journal = {ACM International Conference Proceeding Series},
volume = {13-17-July-2015},
pages = {98-101},
publisher = {Association for Computing Machinery},
abstract = {When a pregnant woman is guided to a hospital for obstetrics purposes, many outcomes are possible, depending on her current conditions. An improved understanding of these conditions could provide a more direct medical approach by categorizing the different types of patients, enabling a faster response to risk situations, and therefore increasing the quality of services. In this case study, the characteristics of the patients admitted in the maternity care unit of Centro Hospitalar of Porto are acknowledged, allowing categorizing the patient women through clustering techniques. The main goal is to predict the patients' route through the maternity care, adapting the services according to their conditions, providing the best clinical decisions and a cost-effective treatment to patients. The models developed presented very interesting results, being the best clustering evaluation index: 0.65. The evaluation of the clustering algorithms proved the viability of using clustering based data mining models to characterize pregnant patients, identifying which conditions can be used as an alert to prevent the occurrence of medical complications. © Copyright 2015 ACM.},
note = {cited By 6; Conference of 8th International C Conference on Computer Science and Software Engineering, C3S2E 2015 ; Conference Date: 13 July 2015 Through 15 July 2015; Conference Code:116074},
keywords = {Artificial intelligence; C (programming language); Cost effectiveness; Data mining; Decision support systems; Interoperability; Medical computing; Obstetrics; Patient treatment; Software engineering, Clustering algorithms, Clustering; Clustering evaluation; Clustering techniques; Cost effective treatments; Data mining models; Maternity care; Medical complications; Real data},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Abelha201510,
title = {Improving quality of services in maternity care triage system},
author = {A. Abelha and E. Pereira and A. Brandão and F. Portela and M. F. Santos and J. Machado and J. Braga},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84944457692&doi=10.4018%2fIJEHMC.2015040102&partnerID=40&md5=ff563e8cb30780f5f0d1073b0846793b},
doi = {10.4018/IJEHMC.2015040102},
issn = {1947315X},
year  = {2015},
date = {2015-01-01},
journal = {International Journal of E-Health and Medical Communications},
volume = {6},
number = {2},
pages = {10-26},
publisher = {IGI Global},
abstract = {The main objectives in triage are to improve the quality of care and reduce the risks associated to the waiting time in emergency care. Thus, an efficient triage is a good way to avoid some future problems and how much quicker it is, more the patient can benefit. The most common triage system is the Manchester Triage System that is a reliable system focused in the emergency department of a hospital. However, its use is more suitable for more widespread medical emergencies and not for specialized cases, like Gynecological and Obstetrics emergencies. To overcome these limitations, an alternative pre-triage system, integrated into an intelligent decision support system, was developed in order to better characterize the patient and correctly defined her as urgent or not. This system allows the increase of patient's safety, especially women who need immediate care. This paper includes the workflow that describes the decision process in real time in the emergency department, when women are submitted to triage and identify points of evolution.},
note = {cited By 10},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Portela20152,
title = {Predict hourly patient discharge probability in intensive care units using Data Mining},
author = {F. Portela and R. Veloso and O. Oliveira and M. F. Santos and A. Abelha and J. Machado and A. Silva and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85008199767&doi=10.17485%2fijst%2f2015%2fv8i32%2f92043&partnerID=40&md5=b33a2c2b825e47f65ac3513ff8b48c77},
doi = {10.17485/ijst/2015/v8i32/92043},
issn = {09746846},
year  = {2015},
date = {2015-01-01},
journal = {Indian Journal of Science and Technology},
volume = {8},
number = {32},
pages = {2-11},
publisher = {Indian Society for Education and Environment},
abstract = {The length of stay (LOS) is an important metric to manage hospital units since a correct prevision of the LOS can contribute to reduce costs and optimize resources. This metric become more fundamental in intensive care units (ICU) where controlling patient condition and predict clinical events is very difficult. A set of experiences was made using data mining techniques in order to predict something more ambitious than LOS. Using the data provided by INTCare system it was possible to induce models with a very good sensitivity (95%) in order to predict the probability of a patient be discharged in the next hour. The results achieved also allow for predicting the bed occupancyrate in ICU for the next hour. The work done represents a novelty in this area and contributes to improve the decision making process providing new knowledge in real time.},
note = {cited By 18},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Neves201565,
title = {A case based approach to concrete deterioration assessment},
author = {J. Neves and G. Gomes and J. Machado and H. Vicente},
editor = {Goto T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983621556&partnerID=40&md5=a1be21d081d6be22a5b10e366066bed3},
isbn = {9781510812284},
year  = {2015},
date = {2015-01-01},
journal = {28th International Conference on Computer Applications in Industry and Engineering, CAINE 2015},
pages = {65-70},
publisher = {International Society of Computers and Their Applications (ISCA)},
abstract = {The deterioration of concrete infrastructures is of concern since maintenance and repair require large amounts of resources. It is a multifaceted and complex phenomenon, with multiple causes, namely age, use, maintenance, type of environmental exposure and aggression by biological, chemical, mechanical and physical agents. However, it may be prevented if proactive strategies were embraced (e.g., taking into account similar past experiences). Indeed, this work will start with the development of a decision support system to prevent these events from happening, centered on a formal framework based on Logic Programming for knowledge representation, complemented with a Case-Based Reasoning (CBR) approach to problem solving, which caters for the handling of incomplete, unknown, or even contradictory information. The CBR cycle was adapted in order to cater for the developments referred to above, and clustering methods were enforced to distinguish and aggregate collections of historical records in order to reduce the search space and enhance the retrieve phase.},
note = {cited By 0; Conference of 28th International Conference on Computer Applications in Industry and Engineering, CAINE 2015 ; Conference Date: 12 October 2015 Through 14 October 2015; Conference Code:123216},
keywords = {Artificial intelligence; Computer circuits; Concretes; Decision support systems; Deterioration; Knowledge representation; Logic programming; Phase space methods; Problem solving; Reconfigurable hardware; Repair, Case based reasoning, Case-based approach; Case-based reasoning approaches; Clustering methods; Concrete deterioration; Environmental exposure; Knowledge representation and reasoning; Normalization; Similarity analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gomes201564,
title = {Predicting 2-way football results by means of data mining},
author = {J. Gomes and F. Portela and M. F. Santos and A. Abelha and J. MaChado},
editor = {Al-Akaidi M. Ayesh A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963512943&partnerID=40&md5=c42130345bd3d4f35969ad17280f4ae7},
isbn = {9789077381908},
year  = {2015},
date = {2015-01-01},
journal = {29th Annual European Simulation and Modelling Conference 2015, ESM 2015},
pages = {64-69},
publisher = {EUROSIS},
abstract = {In the last decade, has been found an increase in the number of bookmakers, particularly in the online market (ebusiness). It is possible deducing that this activity is profitable for them and consequently damaging to their users. Nowadays, football is considered one of the most popular sports. Regarding the betting world it was acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting website users has been stressed as a problem. In accordance with the stated arises here, an opportunity to explore. This lack gave origin to this research proposal, which is going to address the possibility of existing a way to support the users on their online bets, in order to improve their results and profitability. A football match could be analysed from the perspective of several types of statistical data, which do not have a direct influence on the final match result. This research work has the aim of helping to improve the performance of online football bets, by providing users statistical data that may be important to take into account, at the time of doing their own bets. In this work it was possible introduce data mining models which are able to predict 2-way results (home team win/draw or visitor team win) with 96,2 % of sensitivity and a good level of accuracy (74.8%). These models are prepared to be the base of an Intelligent System.},
note = {cited By 2; Conference of 29th Annual European Simulation and Modelling Conference 2015, ESM 2015 ; Conference Date: 26 October 2015 Through 28 October 2015; Conference Code:117995},
keywords = {2-way result; eBusiness; Football bets; Football betting; Football game; Knowledge discovery in database, Artificial intelligence; Data mining; Decision support systems; Electronic commerce; Forecasting; Intelligent systems; Modal analysis; Profitability; Statistics, Sports},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira2015601,
title = {Predicting Type of Delivery by Identification of Obstetric Risk Factors through Data Mining},
author = {S. Pereira and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Cruz-Cunha M. M. Eduardo V.J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962855663&doi=10.1016%2fj.procs.2015.08.573&partnerID=40&md5=cc8176cfd884fad22b2f8f3993869258},
doi = {10.1016/j.procs.2015.08.573},
issn = {18770509},
year  = {2015},
date = {2015-01-01},
journal = {Procedia Computer Science},
volume = {64},
pages = {601-609},
publisher = {Elsevier B.V.},
abstract = {In Maternity Care, a quick decision has to be made about the most suitable delivery type for the current patient. Guidelines are followed by physicians to support that decision; however, those practice recommendations are limited and underused. In the last years, caesarean delivery has been pursued in over 28% of pregnancies, and other operative techniques regarding specific problems have also been excessively employed. This study identifies obstetric and pregnancy factors that can be used to predict the most appropriate delivery technique, through the induction of data mining models using real data gathered in the perinatal and maternal care unit of Centro Hospitalar of Oporto (CHP). Predicting the type of birth envisions high-quality services, increased safety and effectiveness of specific practices to help guide maternity care decisions and facilitate optimal outcomes in mother and child. In this work was possible to acquire good results, achieving sensitivity and specificity values of 90.11% and 80.05%, respectively, providing the CHP with a model capable of correctly identify caesarean sections and vaginal deliveries. © 2015 The Authors. Published by Elsevier B.V.},
note = {cited By 32; Conference of Conference on ENTERprise Information Systems/International Conference on Project MANagement/Conference on Health and Social Care Information Systems and Technologies, CENTERIS 2015 ; Conference Date: 7 October 2015 Through 9 October 2015; Conference Code:123098},
keywords = {Data mining models; Delivery techniques; High quality service; Maternity Care; Pregnant; Real data;Obstetrics Care; Sensitivity and specificity; Type of delivery, Data mining; Forecasting; Information systems; Interoperability; Obstetrics; Project management, Information management},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Braga2015618,
title = {Step Towards a Patient Timeline in Intensive Care Units},
author = {A. Braga and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},
editor = {Cruz-Cunha M. M. Eduardo V.J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962786835&doi=10.1016%2fj.procs.2015.08.575&partnerID=40&md5=de7870befb4e0ea572d09fd2c91f020e},
doi = {10.1016/j.procs.2015.08.575},
issn = {18770509},
year  = {2015},
date = {2015-01-01},
journal = {Procedia Computer Science},
volume = {64},
pages = {618-625},
publisher = {Elsevier B.V.},
abstract = {In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients' conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed. © 2015 The Authors. Published by Elsevier B.V.},
note = {cited By 4; Conference of Conference on ENTERprise Information Systems/International Conference on Project MANagement/Conference on Health and Social Care Information Systems and Technologies, CENTERIS 2015 ; Conference Date: 7 October 2015 Through 9 October 2015; Conference Code:123098},
keywords = {Clinical history; INTCare; Medical history; Medical information; Patient Timeline; Patient-centered; Patients' conditions; Presenting informations, Information management, Information systems; Information use; Intensive care units; Project management},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2015626,
title = {Intelligent Decision Support to Predict Patient Barotrauma Risk in Intensive Care Units},
author = {S. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},
editor = {Cruz-Cunha M. M. Eduardo V.J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962786672&doi=10.1016%2fj.procs.2015.08.576&partnerID=40&md5=9da1a81f449b781b145fd534e71f6c96},
doi = {10.1016/j.procs.2015.08.576},
issn = {18770509},
year  = {2015},
date = {2015-01-01},
journal = {Procedia Computer Science},
volume = {64},
pages = {626-634},
publisher = {Elsevier B.V.},
abstract = {The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: "risk" and "no risk". Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated. © 2015 The Authors. Published by Elsevier B.V.},
note = {cited By 7; Conference of Conference on ENTERprise Information Systems/International Conference on Project MANagement/Conference on Health and Social Care Information Systems and Technologies, CENTERIS 2015 ; Conference Date: 7 October 2015 Through 9 October 2015; Conference Code:123098},
keywords = {Barotrauma; Decision supports; INTCare; Intensive care; Mechanical ventilation; Patient-centered, Data mining, Decision support systems; Decision trees; Forecasting; Health; Information management; Information systems; Intensive care units; Probability; Project management; Risks; Ventilation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2015174,
title = {Towards an ontology for health complaints management},
author = {A. Oliveira and F. Portela and J. Machado and A. Abelha and J. M. Neves and S. Vaz and A. Silva and M. F. Santos},
editor = {Aveiro D. Dietz J. Fred A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960946193&doi=10.5220%2f0005594901740181&partnerID=40&md5=413b8b201fc0fa3882fad7e7a695fa50},
doi = {10.5220/0005594901740181},
isbn = {9789897581588},
year  = {2015},
date = {2015-01-01},
journal = {IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management},
volume = {3},
pages = {174-181},
publisher = {SciTePress},
abstract = {The dissatisfaction of healthcare institutions users has increased in Portugal in the recent years. This fact can be seen through the increase of complaints that the entity responsible in this country has been receiving lately. More and more technical efforts has been done to understand and analyse this tendency. In this paper the authors are proposing an ontology about the whole process of complaints management from healthcare institutions. All the work was developed after analysing the entire process and the data collected by the entity responsible with this matter in Portugal. The ontology developed can show the main concepts involved in the process and the relationship between them. As main ontology entities are person, document, measure and status. © 2015 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
note = {cited By 4; Conference of 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015 ; Conference Date: 12 November 2015 Through 14 November 2015; Conference Code:117182},
keywords = {Health care; Information systems; Knowledge engineering; Knowledge management, Healthcare institutions; Portugal; Technical efforts; Whole process, Ontology},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela201591,
title = {A real-time intelligent system for tracking patient condition},
author = {F. Portela and S. Oliveira and M. Santos and J. Machado and A. Abelha},
editor = {Hervas R. Bravo J. Villarreal V.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954150793&doi=10.1007%2f978-3-319-26508-7_9&partnerID=40&md5=bbb1e3995de619ba1b102b4dcccf14e9},
doi = {10.1007/978-3-319-26508-7_9},
issn = {03029743},
year  = {2015},
date = {2015-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9456},
pages = {91-97},
publisher = {Springer Verlag},
abstract = {Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance. © Springer International Publishing Switzerland 2015.},
note = {cited By 7; Conference of 1st International Conference on Ambient Intelligence for Health, AmIHEALTH 2015 ; Conference Date: 1 December 2015 Through 4 December 2015; Conference Code:159599},
keywords = {Ambient intelligence; Critic events; Data streaming; Intcare; Intensive care; Real time; Tracking system, Artificial intelligence; Data reduction; Embedded systems; Health care; Intelligent systems, Real time systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela201577,
title = {Real-time decision support using data mining to predict blood pressure critical events in intensive medicine patients},
author = {F. Portela and M. F. Santos and J. Machado and A. Abelha and F. Rua and Á. Silva},
editor = {Hervas R. Bravo J. Villarreal V.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954110962&doi=10.1007%2f978-3-319-26508-7_8&partnerID=40&md5=4b2b9925c5d4064a973f40f5a4636dff},
doi = {10.1007/978-3-319-26508-7_8},
issn = {03029743},
year  = {2015},
date = {2015-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9456},
pages = {77-90},
publisher = {Springer Verlag},
abstract = {Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%. © Springer International Publishing Switzerland 2015.},
note = {cited By 13; Conference of 1st International Conference on Ambient Intelligence for Health, AmIHEALTH 2015 ; Conference Date: 1 December 2015 Through 4 December 2015; Conference Code:159599},
keywords = {Artificial intelligence; Blood pressure; Decision support systems; Hospital data processing; Intensive care units, Continuous monitoring; Critical events; Decision supports; Intcare; Mining classification; Patient condition; Real time; Real time decisions, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Guimarães2015561,
title = {Step towards Multiplatform Framework for supporting Pediatric and Neonatology Care Unit decision process},
author = {T. Guimarães and C. Coimbra and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Shakshuki E.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954108776&doi=10.1016%2fj.procs.2015.08.385&partnerID=40&md5=3828c90744a80f9b0b92da1d53946dce},
doi = {10.1016/j.procs.2015.08.385},
issn = {18770509},
year  = {2015},
date = {2015-01-01},
journal = {Procedia Computer Science},
volume = {63},
pages = {561-568},
publisher = {Elsevier B.V.},
abstract = {Children are an especially vulnerable population, particularly in respect to drug administration. It is estimated that neonatal and pediatric patients are at least three times more vulnerable to damage due to adverse events and medication errors than adults are. With the development of this framework, it is intended the provision of a Clinical Decision Support System based on a prototype already tested in a real environment. The framework will include features such as preparation of Total Parenteral Nutrition prescriptions, table pediatric and neonatal emergency drugs, medical scales of morbidity and mortality, anthropometry percentiles (weight, length/height, head circumference and BMI), utilities for supporting medical decision on the treatment of neonatal jaundice and anemia and support for technical procedures and other calculators and widespread use tools. The solution in development means an extension of INTCare project. The main goal is to provide an approach to get the functionality at all times of clinical practice and outside the hospital environment for dissemination, education and simulation of hypothetical situations. The aim is also to develop an area for the study and analysis of information and extraction of knowledge from the data collected by the use of the system. This paper presents the architecture, their requirements and functionalities and a SWOT analysis of the solution proposed. © 2015 The Authors.},
note = {cited By 1; Conference of 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2015 ; Conference Date: 27 September 2015 Through 30 September 2015; Conference Code:117631},
keywords = {Artificial intelligence; Decision support systems; Scales (weighing instruments), INTCare; Intensive care; Multi-platform; Neonatology; Person-centered; SWOT analysis, Pediatrics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira2015292,
title = {Improving quality of medical service with mobile health software},
author = {A. Pereira and F. Marins and B. Rodrigues and F. Portela and M. F. Santos and J. Machado and F. Rua and Á. Silva and A. Abelha},
editor = {Shakshuki E.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954103763&doi=10.1016%2fj.procs.2015.08.346&partnerID=40&md5=b23116ebee8f05124397f1ce5fbb871c},
doi = {10.1016/j.procs.2015.08.346},
issn = {18770509},
year  = {2015},
date = {2015-01-01},
journal = {Procedia Computer Science},
volume = {63},
pages = {292-299},
publisher = {Elsevier B.V.},
abstract = {An increasing number of m-Health applications are being developed benefiting health service delivery. In this paper, a new methodology based on the principle of calm computing applied to diagnostic and therapeutic procedure reporting is proposed. A mobile application was designed for the physicians of one of the Portuguese major hospitals, which takes advantage of a multiagent interoperability platform, the Agency for the Integration, Diffusion and Archive (AIDA). This application allows the visualization of inpatients and outpatients medical reports in a quicker and safer manner, in addition to offer a remote access to information. This project shows the advantages in the use of mobile software in a medical environment but the first step is always to build or use an interoperability platform, flexible, adaptable and pervasive. The platform offers a comprehensive set of services that restricts the development of mobile software almost exclusively to the mobile user interface design. The technology was tested and assessed in a real context by intensivists. © 2015 The Authors.},
note = {cited By 16; Conference of 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2015 ; Conference Date: 27 September 2015 Through 30 September 2015; Conference Code:117631},
keywords = {Calm computing; Clinical information; Health care professionals; Healthcare quality; INTCare; mHealth; Pervasive systems; Ubiquitouse, Diagnosis; Health; Health care; Interoperability; Mobile telecommunication systems; Ubiquitous computing, User interfaces},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2015122,
title = {Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables},
author = {S. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},
editor = {Costa E. Machado P. Pereira F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945970524&doi=10.1007%2f978-3-319-23485-4_13&partnerID=40&md5=8d9e4358d2a397a7ddba9da8359d63bf},
doi = {10.1007/978-3-319-23485-4_13},
issn = {03029743},
year  = {2015},
date = {2015-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9273},
pages = {122-127},
publisher = {Springer Verlag},
abstract = {Predicting barotrauma occurrence in intensive care patients is a difficult task. Data Mining modelling can contribute significantly to the identification of patients who will suffer barotrauma. This can be achieved by grouping patient data, considering a set of variables collected from ventilators directly related with barotrauma, and identifying similarities among them. For clustering have been considered k-means and k-medoids algortihms (Partitioning Around Medoids). The best model induced presented a Davies-Bouldin Index of 0.64. This model identifies the variables that have more similarity among the variables monitored by the ventilators and the occurrence of barotrauma. © Springer International Publishing Switzerland 2015.},
note = {cited By 1; Conference of 17th Portuguese Conference on Artificial Intelligence, EPIA 2015 ; Conference Date: 8 September 2015 Through 11 September 2015; Conference Code:140439},
keywords = {Artificial intelligence; Correlation methods; Hospital data processing; Intensive care units, Barotrauma; Clustering; Davies-Bouldin index; Intensive-care patients; Partitioning around medoids; Patient data; Plateau pressures; Similarity, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira2015116,
title = {Predicting preterm birth in maternity care by means of data mining},
author = {S. Pereira and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Costa E. Machado P. Pereira F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945926609&doi=10.1007%2f978-3-319-23485-4_12&partnerID=40&md5=49364e9e0ed601bbcecbef6c655affb4},
doi = {10.1007/978-3-319-23485-4_12},
issn = {03029743},
year  = {2015},
date = {2015-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9273},
pages = {116-121},
publisher = {Springer Verlag},
abstract = {Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively. © Springer International Publishing Switzerland 2015.},
note = {cited By 1; Conference of 17th Portuguese Conference on Artificial Intelligence, EPIA 2015 ; Conference Date: 8 September 2015 Through 11 September 2015; Conference Code:140439},
keywords = {Artificial intelligence; Decision making; Forecasting; Obstetrics, Data mining, Data mining models; Decision making process; Maternity care; Preterm birth; Preterm deliveries; Real data; Real environments; Sensitivity and specificity},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neves201571,
title = {Artificial neural networks in diagnosis of liver diseases},
author = {J. Neves and A. Cunha and A. Almeida and A. Carvalho and J. Neves and A. Abelha and J. Machado and H. Vicente},
editor = {Khuri S. Holzinger A. Elena Renda M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943624859&doi=10.1007%2f978-3-319-22741-2_7&partnerID=40&md5=828f2d651a066de63ed81f6d70e92325},
doi = {10.1007/978-3-319-22741-2_7},
issn = {03029743},
year  = {2015},
date = {2015-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9267},
pages = {71-80},
publisher = {Springer Verlag},
abstract = {Liver diseases have severe patients’ consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks. © Springer International Publishing Switzerland 2015.},
note = {cited By 5; Conference of 6th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2015 ; Conference Date: 3 September 2015 Through 4 September 2015; Conference Code:139329},
keywords = {Artificial neuronal network; Diagnosis support systems; Early diagnosis; Formal framework; Knowledge representation and reasoning; Liver disease; Premature death, Computation theory; Health care; Information science; Knowledge representation; Logic programming; Neural networks; Neurons; Program diagnostics, Diagnosis},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Veloso2015582,
title = {Using domain knowledge to improve intelligent decision support in intensive medicine: A study of bacteriological infections},
author = {R. Veloso and F. Portela and M. F. Santos and Á. Silva and F. Rua and A. Abelha and J. Machado},
editor = {Filipe J. Filipe J. Loiseau S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943257423&doi=10.5220%2f0005286405820587&partnerID=40&md5=9915e188ba2224ad3a6278075d5747da},
doi = {10.5220/0005286405820587},
isbn = {9789897580741},
year  = {2015},
date = {2015-01-01},
journal = {ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, Proceedings},
volume = {2},
pages = {582-587},
publisher = {SciTePress},
abstract = {Nowadays antibiotic prescription is object of study in many countries. The rate of prescription varies from country to country, without being found the reasons that justify those variations. In intensive care units the number of new infections rising each day is caused by multiple factors like inpatient length of stay, low defences of the body, chirurgical infections, among others. In order to complement the support of the decision process about which should be the most efficient antibiotic it was developed a heuristic based in domain knowledge extracted from biomedical experts. This algorithm is implemented by intelligent agents. When an alert appear on the presence of a new infection, an agent collects the microbiological results for cultures, it permits to identify the bacteria, then using the rules it searches for a role of antibiotics that can be administered to the patient, based on past results. At the end the agent presents to physicians the top-five sets and the success percentage of each antibiotic. This paper presents the approach proposed and a test with a particular bacterium using real data provided by an Intensive Care Unit.},
note = {cited By 2; Conference of 7th International Conference on Agents and Artificial Intelligence, ICAART 2015 ; Conference Date: 10 January 2015 Through 12 January 2015; Conference Code:112667},
keywords = {Antibiotics; Artificial intelligence; Bacteria; Intelligent agents; Intensive care units, Decision support systems, Decision supports; Heuristics; Infections; Intcare; Therapies},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Brandão2015594,
title = {Predicting the risk associated to pregnancy using Data Mining},
author = {A. Brandão and E. Pereira and F. Portela and M. Santos and A. Abelha and J. Machado},
editor = {Filipe J. Filipe J. Loiseau S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943227689&doi=10.5220%2f0005286805940601&partnerID=40&md5=1a23bacaf6154af6a890cd1fcc2bfafa},
doi = {10.5220/0005286805940601},
isbn = {9789897580741},
year  = {2015},
date = {2015-01-01},
journal = {ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, Proceedings},
volume = {2},
pages = {594-601},
publisher = {SciTePress},
abstract = {Woman willing to terminate pregnancy should in general use a specialized health unit, as it is the case of Maternidade Júlio Dinis in Porto, Portugal. One of the four stages comprising the process is evaluation. The purpose of this article is to evaluate the process of Voluntary Termination of Pregnancy and, consequently, identify the risk associated to the patients. Data Mining (DM) models were induced to predict the risk in a real environment. Three different techniques were considered: Decision Tree (DT), Support Vector Machine (SVM) and Generalized Linear Models (GLM) to perform the classification task. Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was applied to drive this work. Very promising results were obtained, achieving a sensitivity of approximately 93%.},
note = {cited By 12; Conference of 7th International Conference on Agents and Artificial Intelligence, ICAART 2015 ; Conference Date: 10 January 2015 Through 12 January 2015; Conference Code:112667},
keywords = {Artificial intelligence; Competitive intelligence; Decision support systems; Decision trees; Digital storage; Obstetrics; Support vector machines, Classification tasks; Data mining models; Generalized linear model; Intelligent decision support systems; Real environments; Technology acceptance; Three different techniques; Voluntary interruption of pregnancy, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Vilhena201551,
title = {Thrombophilia screening: An artificial neural network approach},
author = {J. Vilhena and M. R. Martins and H. Vicente and L. Nelas and J. Machado and J. Neves},
editor = {Fred A. Bienkiewicz M. Verdier C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938860336&doi=10.5220%2f0005197500510059&partnerID=40&md5=7b9d3e6a1f572fde96d2c3e02e237a6b},
doi = {10.5220/0005197500510059},
isbn = {9789897580680},
year  = {2015},
date = {2015-01-01},
journal = {HEALTHINF 2015 - 8th International Conference on Health Informatics, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015},
pages = {51-59},
publisher = {SciTePress},
abstract = {Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.},
note = {cited By 0; Conference of 8th International Conference on Health Informatics, HEALTHINF 2015 ; Conference Date: 12 January 2015 Through 15 January 2015; Conference Code:112647},
keywords = {Artificial neural network approach; Causes of death; Computational framework; Individual risks; Knowledge representation and reasoning; Risk evaluation; Thrombotic disorders, Biomedical engineering; Computation theory; Computer circuits; Decision support systems; Diagnosis; Knowledge representation; Logic programming; Neural networks, Medical informatics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neves2015492,
title = {An evaluation of parchments' degradation a hybrid approach},
author = {J. Neves and J. Machado and G. Gomes and S. Sousa and D. Tereso and A. Coelho and A. T. Caldeira and A. Pereira and A. Candeias and H. Vicente},
editor = {Georgieva P. Spasov G. Mladenov V.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938703478&doi=10.7148%2f2015-0492&partnerID=40&md5=6b998f0f100e8eb85976d0dd1a677702},
doi = {10.7148/2015-0492},
isbn = {9780993244001},
year  = {2015},
date = {2015-01-01},
journal = {Proceedings - 29th European Conference on Modelling and Simulation, ECMS 2015},
pages = {492-498},
publisher = {European Council for Modelling and Simulation},
abstract = {Parchment stands for a multifaceted material made from animal skin, which has been used for centuries as a writing support or as bookbinding. Due to the historic value of objects made of parchment, understanding their degradation and their condition is of utmost importance to archives, libraries and museums, i.e., the assessment of parchment degradation is mandatory, although it is hard to do with traditional methodologies and tools for problem solving. Hence, in this work we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate Parchment Degradation and the respective Degree-of-Confidence that one has on such a happening.},
note = {cited By 0; Conference of 29th European Conference on Modelling and Simulation, ECMS 2015 ; Conference Date: 26 May 2015 Through 29 May 2015; Conference Code:112782},
keywords = {Animal skins; Degree of confidence; Formal framework; Hybrid approach; Hybrid decision support systems; Knowledge representation and reasoning, Computation theory; Decision support systems; Knowledge representation; Logic programming; Neural networks; Problem solving, Computer circuits},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neves2015211,
title = {Logic programming and artificial neural networks in breast cancer detection},
author = {J. Neves and T. Guimarães and S. Gomes and H. Vicente and M. Santos and J. Neves and J. Machado and P. Novais},
editor = {Catala A. Joya G. Rojas I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937690305&doi=10.1007%2f978-3-319-19222-2_18&partnerID=40&md5=4892cc38432d002443cd457e3c466108},
doi = {10.1007/978-3-319-19222-2_18},
issn = {03029743},
year  = {2015},
date = {2015-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9095},
pages = {211-224},
publisher = {Springer Verlag},
abstract = {About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening. © Springer International Publishing Switzerland 2015.},
note = {cited By 9; Conference of 13th International Work-Conference on Artificial Neural Networks, IWANN 2015 ; Conference Date: 10 June 2015 Through 12 June 2015; Conference Code:119669},
keywords = {Artificial intelligence; Computation theory; Decision support systems; Diseases; Knowledge representation; Neural networks; Risk assessment, Breast Cancer; Breast cancer detection; Breast cancer risk assessments; Degree of confidence; Formal framework; Hybrid decision support systems; Knowledge representation and reasoning; Logic programing, Logic programming},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira2015155,
title = {An overview of the quality of service in bluetooth communications in healthcare},
author = {A. Pereira and E. Pereira and E. Silva and T. Guimarães and F. Portela and M. F. Santos and A. Abelha and J. Machado},
editor = {Gonzalez G. V. Fernandez-Caballero A. Novais P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937431981&doi=10.1007%2f978-3-319-19695-4_16&partnerID=40&md5=6afad0b1292d5acafaabe7485585ce42},
doi = {10.1007/978-3-319-19695-4_16},
issn = {21945357},
year  = {2015},
date = {2015-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {376},
pages = {155-164},
publisher = {Springer Verlag},
abstract = {Currently, the general public requires devices getting faster and great performance, that is, devices ensuring a better quality of service. One way to achieve these goals is through the use of devices supported by the mobile computing with tools to help the search for information. Bluetooth technology is an open standard for wireless communication allowing the transmission of data and information between electronic devices within walking distance, with minimum resource expenditures, safe and rapid transition of data. So, the Bluetooth technology was initially designed to support simple network devices and personal devices such as mobile phones, PDAs and computers, but quickly it were discovered other applications in several areas. In this article, it will be performed a literature review on the topic, with the goal to understand how the Bluetooth technology can benefit increases in the Quality of Service and the presentation of some actual and potential biomedical applications. © Springer International Publishing Switzerland 2015.},
note = {cited By 0; Conference of 6th International Symposium on Ambient Intelligence, ISAmI 2015 ; Conference Date: 3 June 2015 Through 5 June 2015; Conference Code:119489},
keywords = {Application programs; Artificial intelligence; Bluetooth; Electron devices; Medical applications; Personal computers; Quality of service; Wireless telecommunication systems, Biomedical applications; Heal; Master; Piconets; Slave; Ubiquitous devices, Cellular telephone systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Lima2015679,
title = {Big data for stock market by means of mining techniques},
author = {L. Lima and F. Portela and M. F. Santos and A. Abelha and J. Machado},
editor = {Correia A. M. Rocha A. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926313922&doi=10.1007%2f978-3-319-16486-1_67&partnerID=40&md5=3c0f77005ba2f642b35e948510de3797},
doi = {10.1007/978-3-319-16486-1_67},
issn = {21945357},
year  = {2015},
date = {2015-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {353},
pages = {679-688},
publisher = {Springer Verlag},
abstract = {Predict and prevent future events are the major advantages to any company. Big Data comes up with huge power, not only by the ability of processes large amounts and variety of data at high velocity, but also by the capability to create value to organizations. This paper presents an approach to a Big Data based decision making in the stock market context. The correlation between news articles and stock variations it is already proved but it can be enriched with other indicators. In this use case they were collected news articles from three different web sites and the stock history from the New York Stock Exchange. In order to proceed to data mining classification algorithms the articles were labeled by their sentiment, the direct relation to a specific company and geographic market influence. With the proposed model it is possible identify the patterns between this indicators and predict stock price variations with accuracies of 100 percent. Moreover the model shown that the stock market could be sensitive to news with generic topics, such as government and society but they can also depend on the geographic cover. © Springer International Publishing Switzerland 2015.},
note = {cited By 6; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115919},
keywords = {Big data, Commerce; Data mining; Decision making; Electronic document exchange; Finance; Financial markets; Forecasting; Information systems, Data mining classification algorithms; Geographic market; Large amounts; Mining techniques; New York Stock Exchange; News articles; Stock predictions; Text mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Carvalho2015361,
title = {Step towards of a homemade business intelligence solution - A case study in textile industry},
author = {S. Carvalho and F. Portela and M. F. Santos and A. Abelha and J. Machado},
editor = {Correia A. M. Rocha A. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926297419&doi=10.1007%2f978-3-319-16486-1_36&partnerID=40&md5=ca7e82e653f187516f2d71c5fb3585b5},
doi = {10.1007/978-3-319-16486-1_36},
issn = {21945357},
year  = {2015},
date = {2015-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {353},
pages = {361-370},
publisher = {Springer Verlag},
abstract = {Nowadays, organizations are increasingly looking to invest in business intelligence solutions, mainly private companies in order to get advantage over its competitors, however they do not know what is necessary. Business intelligence allows an analysis of consolidated information in order to obtain more specific outlets and certain indications in order to support the decision making process. You can take the right decision based on the data collected from different information systems present in the organization and outside of them. The textile sector is a sector where concept of Business Intelligence it is not many explored yet. Actually there are few textile companies that have a BI platform. Thus, the article objective is present an architecture and show all the steps by which companies need to spend to implement a successful free homemade Business Intelligence system. As result the proposed approach it was validated using real data aiming assess the steps defined. © Springer International Publishing Switzerland 2015.},
note = {cited By 5; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115919},
keywords = {Architecture; Data warehouses; Decision making; Decision support systems; Information analysis; Information systems; Textiles, Business intelligence systems; Decision making process; Decision supports; Decision-based; Free software; Private companies; Textile sector, Textile industry},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neves2015179,
title = {An assessment of chronic kidney diseases},
author = {J. Neves and M. Rosário Martins and H. Vicente and J. Neves and A. Abelha and J. Machado},
editor = {Correia A. M. Rocha A. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926295620&doi=10.1007%2f978-3-319-16486-1_18&partnerID=40&md5=ed38e161692c5017fd25d6a9f5d92586},
doi = {10.1007/978-3-319-16486-1_18},
issn = {21945357},
year  = {2015},
date = {2015-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {353},
pages = {179-191},
publisher = {Springer Verlag},
abstract = {Once kidney disease is exposed, the presence or degree of kidney dysfunction and its progression are assessed, and the underlying syndrome may be diagnosed. Although the patient`s history and corporeal examination may be useful, some key information is obtained from valuation of the Glomerular Filtration Rate, and analysis of the urinary sediment. On the one hand, Chronic Kidney Diseases (CKDs) depicts anomalous kidney function and/or its makeup. On the other hand, there is evidence that treatment may avoid or delay the progression of CKDs, either by reducing and prevent the development of complications, or by reducing the risk of CardioVascular Illnesses. Acute Renal Failure (ARF) can occur over hours to days based on the underlying mechanism of injury and relative health of the individual. ARF is often reversible if it is recognized early and treated promptly. This is the reason behind our compromise in presenting this work, that aims at the development of an early diagnosis system to monitor the occurrence of the disease, and therefore to allow one to act proactively. © Springer International Publishing Switzerland 2015.},
note = {cited By 2; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115919},
keywords = {Artificial neuronal network; Chronic kidney disease; Glomerular filtration rate; Kidney dysfunction; Kidney function; Knowledge representation and reasoning; Mechanism of injury; Urinary sediment, Diagnosis, Health care; Information systems; Knowledge representation; Logic programming; Neurons},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Novo2015199,
title = {Information systems assessment in pathologic anatomy service},
author = {A. Novo and J. Duarte and F. Portela and A. Abelha and M. F. Santos and J. Machado},
editor = {Reis L. P. Rocha A. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925337385&doi=10.1007%2f978-3-319-16528-8_19&partnerID=40&md5=9f5488f3ef9fed4ed88870923899a1df},
doi = {10.1007/978-3-319-16528-8_19},
issn = {21945357},
year  = {2015},
date = {2015-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {354},
pages = {199-209},
publisher = {Springer Verlag},
abstract = {Information technologies changed the way of how the health organizations work, contributing to their effectiveness, efficiency and sustainability. Hospital Information Systems (HIS) are emerging on all of health institutions, helping health professionals and patients. However, HIS are not always implemented and used in the best way, leading to low levels of benefits and acceptance by users of these systems. In order to mitigate this problem, it is essential to take measures able to ensure if the HIS and their interfaces are designed in a simple and interactive way. With this in mind, a study to measure the user satisfaction and their opinion was made. It was applied the Technology Acceptance Model (TAM) on a HIS implemented on various hospital centers (AIDA), being used the Pathologic Anatomy Service. The study identified weakness and strengths features of AIDA and it pointed some solutions to improve the medical record. © Springer International Publishing Switzerland 2015.},
note = {cited By 5; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115559},
keywords = {Health; Hospitals; Information systems; Pathology, Hospital information systems; Pathologic anatomy service; TAM; Technology acceptance; Technology assessments, Medical computing},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Cardoso2015,
title = {Abstract computation in schizophrenia detection through artificial neural network based systems},
author = {L. Cardoso and F. Marins and R. Magalhães and N. Marins and T. Oliveira and H. Vicente and A. Abelha and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925337170&doi=10.1155%2f2015%2f467178&partnerID=40&md5=c13f21c4549021d0b90098210958ddd4},
doi = {10.1155/2015/467178},
issn = {23566140},
year  = {2015},
date = {2015-01-01},
journal = {Scientific World Journal},
volume = {2015},
publisher = {Hindawi Publishing Corporation},
abstract = {Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information. © 2015 L. Cardoso et al.},
note = {cited By 7},
keywords = {Adolescent; Adult; Aged; Child; Child, Article; artificial neural network; diagnostic reasoning; human; knowledge; mathematical analysis; medical research; model; schizophrenia; adolescent; adult; aged; child; female; genetic predisposition; genetics; infant; male; middle aged; newborn; preschool child; schizophrenia; young adult, Newborn; Male; Middle Aged; Neural Networks (Computer); Schizophrenia; Young Adult, Preschool; Female; Genetic Predisposition to Disease; Humans; Infant; Infant},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Silva2015189,
title = {Predicting nosocomial infection by using data mining technologies},
author = {E. Silva and L. Cardoso and F. Portela and A. Abelha and M. F. Santos and J. Machado},
editor = {Reis L. P. Rocha A. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925325130&doi=10.1007%2f978-3-319-16528-8_18&partnerID=40&md5=043beaeb8726c7a044de6bc1cc09a0ef},
doi = {10.1007/978-3-319-16528-8_18},
issn = {21945357},
year  = {2015},
date = {2015-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {354},
pages = {189-198},
publisher = {Springer Verlag},
abstract = {The existence of nosocomial infection prevision systems in healthcare environments can contribute to improve the quality of the healthcare institution and also to reduce the costs with the treatment of the patients that acquire these infections. The analysis of the information available allows to efficiently prevent these infections and to build knowledge that can help to identify their eventual occurrence. This paper presents the results of the application of predictive models to real clinical data. Good models, induced by the Data Mining (DM) classification techniques Support Vector Machines and Naïve Bayes, were achieved (sensitivities higher than 91.90%). Therefore, with these models that be able to predict these infections may allow the prevention and, consequently, the reduction of nosocomial infection incidence. They should act as a Clinical Decision Support System (CDSS) capable of reducing nosocomial infections and the associated costs, improving the healthcare and, increasing patients’ safety and well-being. © Springer International Publishing Switzerland 2015.},
note = {cited By 11; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115559},
keywords = {Artificial intelligence; Cost reduction; Data mining; Health care; Information systems; Patient treatment, Classification technique; Clinical decision support systems; CRISP-DM; Data mining technology; Healthcare environments; Healthcare institutions; Knowledge discovery in database; Nosocomial infection, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Teixeira2015155,
title = {Decision support in E-government – a pervasive business intelligence approach case study in a local government},
author = {R. Teixeira and F. Afonso and B. Oliveira and J. Machado and A. Abelha and M. F. Santos and F. Portela},
editor = {Reis L. P. Rocha A. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925308062&doi=10.1007%2f978-3-319-16528-8_15&partnerID=40&md5=e9d9d52e3e6395f0d203fb789130caa8},
doi = {10.1007/978-3-319-16528-8_15},
issn = {21945357},
year  = {2015},
date = {2015-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {354},
pages = {155-166},
publisher = {Springer Verlag},
abstract = {Business Intelligence (BI) systems are being increasingly used by organizations and considered as an advantage, which goal is to offer access to information in a timely manner to support the decision-making process. Simultaneously, the local government has put forward quality assurance systems, with the goal of improving efficiency of internal processes, who require timely and quality information to function. However it should be noted that this is an area of activity with peculiar characteristics that must be taken into account. This paper presents a real case study and the development of a pervasive BI functional solution implemented in local government, providing support and improving the quality of processes. The developed solution brings some important contributions and represents some advances in the e-Government context applied to local governments. This solution is able to accommodate a wide range of users, with the information available anytime and anywhere, capable of issuing alerts and being ubiquitous, scalable and have real-time data availability. © Springer International Publishing Switzerland 2015},
note = {cited By 10; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115559},
keywords = {Business intelligence systems; Decision making process; E-governments; Improving efficiency; Local government; Quality assurance systems; Quality information; Real time, Competitive intelligence; Decision making; Decision support systems; Information analysis; Information systems; Quality assurance, Government data processing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2015179,
title = {Predicting plateau pressure in intensive medicine for ventilated patients},
author = {S. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},
editor = {Reis L. P. Rocha A. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925302904&doi=10.1007%2f978-3-319-16528-8_17&partnerID=40&md5=4f0053e2bf7bd1d77202356c50690a89},
doi = {10.1007/978-3-319-16528-8_17},
issn = {21945357},
year  = {2015},
date = {2015-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {354},
pages = {179-188},
publisher = {Springer Verlag},
abstract = {Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cmH2O) in a real environment and using real data. The present study explored and assessed the possibility of predicting the Plateau pressure class with high accuracies. The dataset used only contained data provided by the ventilators. The best models are able to predict the Plateau Pressure with an accuracy ranging from 95.52% to 98.71%. © Springer International Publishing Switzerland 2015.},
note = {cited By 7; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115559},
keywords = {Barotrauma; Best model; Health professionals; INTCare; Mechanical ventilation; Plateau pressures; Real environments, Data mining; Information systems; Intensive care units; Ventilation, Forecasting},
pubstate = {published},
tppubtype = {inproceedings}
}
@inbook{Peixoto2014447,
title = {A preventive action management platform in healthcare information systems},
author = {H. Peixoto and A. Abelha and M. Santos and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84956829257&doi=10.4018%2f978-1-4666-7230-7.ch026&partnerID=40&md5=c7f8b067202c5b5139d5871127d5cdc8},
doi = {10.4018/978-1-4666-7230-7.ch026},
isbn = {9781466672314; 1466672307; 9781466672307},
year  = {2014},
date = {2014-01-01},
journal = {Open Source Technology: Concepts, Methodologies, Tools, and Applications},
volume = {1-4},
pages = {447-460},
publisher = {IGI Global},
abstract = {Preventive actions management plays a crucial role in clinical applications, not only for those who depend on data to make decisions, but also for those who monitor the operational and financial impact of the systems. This paper presents an open-source platform, named ScheduleIT, capable of managing preventive routines. The platform is based on an estimation model that determines the optimal time interval for interventions, according to the criticality of the system and the number of non-programmed faults, among others. ScheduleIT has a web-based interface available to a different area end-user, ranging from IT technicians to administrative staff. At this point, the platform covers around 75% of the healthcare systems and it is fully accepted by its main users as a reliable and effective preventive tool. © 2015, IGI Global. All rights reserved.},
note = {cited By 0},
keywords = {Administrative staff; Clinical application; Financial impacts; Health care information system; Health-care system; Management platforms; Open source platforms; Web-based interface, Health care; Information systems; Medical computing; Multimedia systems, Information management},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Portela20141092,
title = {An intelligent approach for open clinical laboratory results in Intensive Care medicine},
author = {F. Portela and M. F. Santos and J. Machado and A. Abelha and A. Silva and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84914146029&doi=10.1109%2fIEEM.2013.6962579&partnerID=40&md5=944aebabde870c7c648766152519b8cb},
doi = {10.1109/IEEM.2013.6962579},
issn = {21573611},
year  = {2014},
date = {2014-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
pages = {1092-1096},
publisher = {IEEE Computer Society},
abstract = {The process of how laboratory tests are made and the results are delivered is very important to the decision making process in medicine. This situation is more critical in areas like Intensive Medicine, where the decision needs to be performed quickly and accurately. Typically, the results are presented in a closed format (document). This format represents a barrier for the implementation of Intelligent Decision Support Systems that make use of laboratory results to feed predictive models to help the Intensive Care professionals in such critical decisions. In order to overcome this limitation an intelligent agent based system has been implemented. Lab results are now collected, processed and used in real-time in an open format contributing to the implementation of an automatic scoring system. © 2013 IEEE.},
note = {cited By 2; Conference of 2013 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2013 ; Conference Date: 10 December 2013 Through 13 December 2013; Conference Code:109355},
keywords = {Agent-based systems; Decision making process; INTCare; Intelligent decision support systems; Intensive care medicines; Laboratory test; Predictive models; Tracking system, Agents; Artificial intelligence; Data acquisition; Decision making; Decision support systems, Laboratories},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Duarte20141052,
title = {Stand-Alone electronic health record},
author = {J. Duarte and G. Pontes and M. Salazar and M. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84914129278&doi=10.1109%2fIEEM.2013.6962571&partnerID=40&md5=b3ab9d1284af4c7316577d9149c7c11b},
doi = {10.1109/IEEM.2013.6962571},
issn = {21573611},
year  = {2014},
date = {2014-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
pages = {1052-1056},
publisher = {IEEE Computer Society},
abstract = {Hospitals have made an effort to ensure the permanent availability of data, with the reliance on the information and scalability at the lowest price. Quality and information access speed are guaranteed. On the other hand, information systems become core systems and progressively clinical paper based records has been dematerialized. The single access to information about patients and electronic media, even in critical situations, are now a reality. Despite the efforts referred to above, there remains the possibility of difficulty in accessing information in case of network or electricity failures, or in the event of a local unpredictable disaster. In this paper, we present an extension to the Electronic Health Record, called stand-Alone module, to ensure the hospital's access to patient's minimal clinical record in a breakdown scenario. © 2013 IEEE.},
note = {cited By 1; Conference of 2013 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2013 ; Conference Date: 10 December 2013 Through 13 December 2013; Conference Code:109355},
keywords = {Clinical records; Core systems; Electronic health record; Electronic media; Information access; Lowest price; Stand -alone, eHealth; Hospitals; Records management, Electronic document exchange},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira20141057,
title = {Analysis of cross-platform development frameworks for a smartphone pediatric application},
author = {R. Oliveira and G. Pontes and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84914129277&doi=10.1109%2fIEEM.2013.6962572&partnerID=40&md5=09d2e56f7749b18967ac3409bffbf66b},
doi = {10.1109/IEEM.2013.6962572},
issn = {21573611},
year  = {2014},
date = {2014-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
pages = {1057-1061},
publisher = {IEEE Computer Society},
abstract = {The number of smartphone users is growing rapidly, including among healthcare professionals [1]. Together with a pediatrician, it was identified the necessity of a mobile tool to support clinical practice and decision. However, in order to reach the majority smartphone users it is necessary to develop applications for multiple platforms. In this article it is done an analysis of the smartphone market-share, a study of some of the most popular smartphone cross-platform development frameworks and finally selected the best framework to develop a pediatric application. © 2013 IEEE.},
note = {cited By 2; Conference of 2013 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2013 ; Conference Date: 10 December 2013 Through 13 December 2013; Conference Code:109355},
keywords = {Clinical practices; Cross platform development; Cross-platform; Health care professionals; Market share; Multiple platforms, Commerce; Competition; Pediatrics; Smartphones, mHealth},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marins20141082,
title = {Extending a patient monitoring system with identification and localisation},
author = {F. Marins and R. Rodrigues and F. Portela and M. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84914113050&doi=10.1109%2fIEEM.2013.6962577&partnerID=40&md5=556dc04f5bb84afe83f367467d7bf947},
doi = {10.1109/IEEM.2013.6962577},
issn = {21573611},
year  = {2014},
date = {2014-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
pages = {1082-1086},
publisher = {IEEE Computer Society},
abstract = {Intensive Care Units (ICUs) are a good environment for the application of intelligent systems in the healthcare area because it requires diagnosing, monitoring, and treatment of patients with critical illness. An intelligent decision support system, named INTCare, was developed and tested in CHP, a hospital center in Oporto. The need to detect the presence or absence of the patient in room, in order to stop the collection of redundant data concerning about the patient vital status led to the development of an RFID localisation and monitoring system - PaLMS, able to uniquely and unambiguously identify a patient and perceive its presence in room, making the process of data collection and alert event more accurate. The solution was the implementation of an intelligent multi-Agent system that connects the Patient Management System module, the INTCare module and the RFID equipment, using the HL7 standard embedded in agents behaviours. © 2013 IEEE.},
note = {cited By 2; Conference of 2013 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2013 ; Conference Date: 10 December 2013 Through 13 December 2013; Conference Code:109355},
keywords = {Ambient intelligence; Data acquisition; Decision support systems; Diagnosis; Intelligent agents; Intelligent systems; Multi agent systems; Patient monitoring; Patient treatment; Radio frequency identification (RFID), Data collection; Intelligent decision support systems; Intelligent multi agent systems; Medical informatics; Monitoring system; Patient management; Patient monitoring systems; Redundant data, Intensive care units},
pubstate = {published},
tppubtype = {inproceedings}
}
@inbook{Silva2014193,
title = {Business intelligence and nosocomial infection decision making},
author = {E. Silva and A. Alpuim and L. Cardoso and F. Marins and C. Quintas and C. F. Portela and M. F. Santos and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946131048&doi=10.4018%2f978-1-4666-6477-7.ch010&partnerID=40&md5=6b84b26387401612ee03872a886f1404},
doi = {10.4018/978-1-4666-6477-7.ch010},
isbn = {9781466664784; 1466664770; 9781466664777},
year  = {2014},
date = {2014-01-01},
journal = {Integration of Data Mining in Business Intelligence Systems},
pages = {193-215},
publisher = {IGI Global},
abstract = {The implementation of Business Intelligence tools in healthcare organizations helps the managers and the healthcare professionals in their decision making process through data manipulation and data analysis. The main goal of this chapter is to evaluate the applicability of the Business Intelligence tools and concepts to healthcare and their performance as a Clinical Decision Support System, analyzing the evolution of nosocomial infection in the Centro Hospitalar do Porto, by defining a set of indicators that can help the nosocomial infection management and inducing Data Mining models to predict the occurrence of nosocomial infections (sensitivity of 91%). The knowledge obtained with the analysis of the indicators and the knowledge obtained with the nosocomial infection prediction can be applied by healthcare professionals in their decision making. Through the analysis of the data collected, Business Intelligence tools help overcome the problems associated with the complexity, heterogeneity, and distributiveness present in the healthcare environment. © 2015, IGI Global.},
note = {cited By 3},
keywords = {Artificial intelligence; Decision support systems; Health care; Information analysis; Information management, Clinical decision support systems; Data manipulations; Data mining models; Decision making process; Health care professionals; Healthcare environments; Healthcare organizations; Nosocomial infection, Decision making},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Pereira2014175,
title = {Pre-triage decision support improvement in maternity care by means of data mining},
author = {E. Pereira and A. Brandão and M. Salazar and C. F. Portela and M. F. Santos and J. Machado and A. Abelha and J. Braga},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946122274&doi=10.4018%2f978-1-4666-6477-7.ch009&partnerID=40&md5=aa8f639672408928c62c7d9bee639a7b},
doi = {10.4018/978-1-4666-6477-7.ch009},
isbn = {9781466664784; 1466664770; 9781466664777},
year  = {2014},
date = {2014-01-01},
journal = {Integration of Data Mining in Business Intelligence Systems},
pages = {175-192},
publisher = {IGI Global},
abstract = {A triage system aims to make a correct characterization of the condition of patients. Because conventional triage systems like Manchester Triage System (MTS) are not suitable for maternity care, a decision model for pre-triaging patients in emergency (URG) and consultation (ARGO) classes was built and incorporated into a Decision Support System (DSS) implemented in Centro Materno Infantil do Norte (CMIN). Complementarily, DSS produces several indicators to support clinical and management decisions. A recent data analysis revealed a bias in the classification of URG cases. Frequently, cases classified as URG correspond to ARGO. This misclassification has been studied by means of Data Mining (DM) techniques in order to improve the pre-triage model and to discover knowledge for developing a new triage system based on waiting times and on a 5-scale of classes. This chapter presents a kind of sensitivity analysis combining input variables in six scenarios and considering four different DM techniques. CRISP-DM methodology was used to conduct the project. © 2015, IGI Global.},
note = {cited By 3},
keywords = {Artificial intelligence; Data mining; Sensitivity analysis, Decision modeling; Decision support system (dss); Decision supports; Input variables; Management decisions; Manchester; Misclassifications; Waiting time, Decision support systems},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Portela2014270,
title = {Knowledge acquisition process for intelligent decision support in critical health care},
author = {F. Portela and A. Cabral and A. Abelha and M. Salazar and C. Quintas and J. Machado and J. Neves and M. F. Santos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945381529&doi=10.4018%2f978-1-4666-6339-8.ch015&partnerID=40&md5=df277fdfc8976eefa8f99d0f68a2a30e},
doi = {10.4018/978-1-4666-6339-8.ch015},
isbn = {9781466663404; 1466663391; 9781466663398},
year  = {2014},
date = {2014-01-01},
journal = {Healthcare Administration: Concepts, Methodologies, Tools, and Applications},
volume = {1},
pages = {270-284},
publisher = {IGI Global},
abstract = {An efficient triage system is a good way to avoid some future problems and benefit the patient. However, a limitation still exists. The triage system is general and not specific to each case. Manchester Triage System is a reliable known system and is focused in the emergency department of a hospital. When applied to specific patients' conditions (such as pregnancy), it has several limitations. To overcome those limitations, an alternative triage system, integrated into an intelligent decision support system, was developed. The system classifies patients according to the severity of their clinical condition, establishing clinical priorities and not diagnosis. According to the urgency of attendance or problem type, it suggests one of three possible categories of the triage. This chapter presents the overall knowledge acquisition cycle associated with the workflow of patient arrival and the inherent decision making process. Results show that this new approach enhances the efficiency and the safety through the appropriate use of resources and by assisting the right patient in the right place, reducing the waiting triage time and the number in general urgency. © 2015 by IGI Global. All rights reserved.},
note = {cited By 6},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@article{Gonçalves201436,
title = {Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine},
author = {J. M. C. Gonçalves and F. Portela and M. F. Santos and Á. Silva and J. Machado and A. Abelha and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919657431&doi=10.4018%2fijhisi.2014070103&partnerID=40&md5=87b5cd193381b7b14a73248581b1bd1c},
doi = {10.4018/ijhisi.2014070103},
issn = {15553396},
year  = {2014},
date = {2014-01-01},
journal = {International Journal of Healthcare Information Systems and Informatics},
volume = {9},
number = {3},
pages = {36-54},
publisher = {IGI Global},
abstract = {Optimal treatments for patients with microbiological problems depend significantly on the ability of the attending physicians to predict sepsis level. A set of Data Mining (DM) models has been developed using forecasting techniques and classification models to aid decision making by physicians about the appropriate, and most effective, therapeutic plan to adopt in specific situations. A combination of Decision Trees, Support Vector Machines and Naïve Bayes classifier were being used to generate the DM models. Confusion Matrix, including associated metrics, and Cross-validation were used to evaluate the models. Associated metrics used to identify the most relevant measures to predict sepsis level and treatment procedures include the analysis of the total error rate, sensitivity, specificity, and accuracy measures. The data used in DM models were collected at the Intensive Care Unit of the Centro Hospitalar do Porto, in Oporto, Portugal. Encapsulated within a supervised learning context, classification models were applied to predict sepsis level and direct the therapeutic plan for patients with sepsis. This work concludes that it was possible to predict sepsis level (2nd and 3rd) with great accuracy (accuracy: 100%), but not for the therapeutic plan (best accuracy level: 62.8%). Copyright © 2014, IGI Global.},
note = {cited By 9},
keywords = {Classification (of information); Decision making; Decision trees; Forecasting; Intensive care units; Predictive analytics; Support vector machines, Classification models; INTCare project; Intensive care; Sepsis level; Therapeutic plans, Data mining},
pubstate = {published},
tppubtype = {article}
}
@inbook{Cardoso201478,
title = {Interoperability in healthcare},
author = {L. Cardoso and F. Marins and C. Quintas and F. Portela and M. F. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946117889&doi=10.4018%2f978-1-4666-6118-9.ch005&partnerID=40&md5=d4e9c79a707a09fdf952d7eecf3af5b3},
doi = {10.4018/978-1-4666-6118-9.ch005},
isbn = {9781466661196; 1466661186; 9781466661189},
year  = {2014},
date = {2014-01-01},
journal = {Cloud Computing Applications for Quality Health Care Delivery},
pages = {78-101},
publisher = {IGI Global},
abstract = {With the advancement of technology, patient information has been being computerized in order to facilitate the work of healthcare professionals and improve the quality of healthcare delivery. However, there are many heterogeneous information systems that need to communicate, sharing information and making it available when and where it is needed. To respond to this requirement the Agency for Integration, Diffusion, and Archiving of medical information (AIDA) was created, a multi-agent and service-based platform that ensures interoperability among healthcare information systems. In order to improve the performance of the platform, beyond the SWOT analysis performed, a system to prevent failures that may occur in the platform database and also in machines where the agents are executed was created. The system has been implemented in the Centro Hospitalar do Porto (one of the major Portuguese hospitals), and it is now possible to define critical workload periods of AIDA, improving high availability and load balancing. This is explored in this chapter. © 2014 by IGI Global. All rights reserved.},
note = {cited By 7},
keywords = {Health care information system; Health care professionals; Heterogeneous information; High availability; Medical information; Patient information; Quality of health care; Sharing information, Health care; Information systems; Interoperability; Medical computing; Multi agent systems, Medical information systems},
pubstate = {published},
tppubtype = {inbook}
}
@article{Cardoso20145349,
title = {The next generation of interoperability agents in healthcare},
author = {L. Cardoso and F. Marins and F. Portela and M. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900870917&doi=10.3390%2fijerph110505349&partnerID=40&md5=e35288526d9a9539da0834fc70cc12a7},
doi = {10.3390/ijerph110505349},
issn = {16617827},
year  = {2014},
date = {2014-01-01},
journal = {International Journal of Environmental Research and Public Health},
volume = {11},
number = {5},
pages = {5349-5371},
publisher = {MDPI},
abstract = {Interoperability in health information systems is increasingly a requirement rather than an option. Standards and technologies, such as multi-agent systems, have proven to be powerful tools in interoperability issues. In the last few years, the authors have worked on developing the Agency for Integration, Diffusion and Archive of Medical Information (AIDA), which is an intelligent, agent-based platform to ensure interoperability in healthcare units. It is increasingly important to ensure the high availability and reliability of systems. The functions provided by the systems that treat interoperability cannot fail. This paper shows the importance of monitoring and controlling intelligent agents as a tool to anticipate problems in health information systems. The interaction between humans and agents through an interface that allows the user to create new agents easily and to monitor their activities in real time is also an important feature, as health systems evolve by adopting more features and solving new problems. A module was installed in Centro Hospitalar do Porto, increasing the functionality and the overall usability of AIDA. © 2014 by the authors; licensee MDPI, Basel, Switzerland.},
note = {cited By 51},
keywords = {Delivery of Health Care; Electronic Health Records; Health Information Systems; Humans; Medical Record Linkage; Portugal; Systems Integration, health care; health monitoring; information technology; technological development},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Portela2014287,
title = {A pervasive intelligent system for scoring MEWS and TISS-28 in intensive care},
author = {F. Portela and M. F. Santos and Á. Silva and J. Machado and A. Abelha and F. Rua},
editor = {Goh J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928230268&doi=10.1007%2f978-3-319-02913-9_73&partnerID=40&md5=512f655e3e01e666bd3666c00d4eec95},
doi = {10.1007/978-3-319-02913-9_73},
issn = {16800737},
year  = {2014},
date = {2014-01-01},
journal = {IFMBE Proceedings},
volume = {43},
pages = {287-290},
publisher = {Springer Verlag},
abstract = {Usually intensive care medicine practice is highly supported on scores as SOFA, SAPS and Glasgow. More recently, two new scores have been considered: MEWS and TISS-28.This paper presents how the Scoring System (SS) of the Intensive Care Unit (ICU) of Centro Hospitalar do Porto, evolved in order to accommodate the new scores. SS can automatically, in real-time and using online learning, provide a set of scores with a minimum human effort. SS is integrated in the Electronic Nursing Record (ENR) used for better comprehension of the patient condition through a set of information available: vital signs graphs, therapeutic plans, interventions, laboratory results and others. Those systems are supported by a pervasive platform for monitoring patient data anywhere and anytime. SS calculates the TISS-28 Score per day and nursing turn and the MEWS per minute, hour and day. These two new scores improve the understanding of the real condition of the patients. SS allows for obtaining automatically and in real-time the following scores: SAPS II, SAPS III, SOFA, GLASGOW, TISS28 and Mews anywhere and anytime. The development of SS only was possible due to a continuous and real-time execution of the data acquisition and data processing. The introduction of MEWS and TISS-28 brought some benefits at level of the decision process and workload and, consequently, to the ICU management process. © Springer International Publishing Switzerland 2014.},
note = {cited By 3; Conference of 15th International Conference on Biomedical Engineering, ICBME 2013 ; Conference Date: 4 December 2013 Through 7 December 2013; Conference Code:117089},
keywords = {Biomedical engineering; Data acquisition; Data handling; Hospital data processing; Intelligent systems; Medicine; Nursing; Patient monitoring; Patient treatment, INTCare; Intensive care medicines; Management process; MEWS; Patient condition; Real time execution; Scoring systems; TISS-28, Intensive care units},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Cardoso2014127,
title = {A multi-agent platform for hospital interoperability},
author = {L. Cardoso and F. Marins and F. Portela and M. Santos and A. Abelha and J. Machado},
editor = {Ramos C. Novais P. Nihan C.E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927780765&doi=10.1007%2f978-3-319-07596-9_14&partnerID=40&md5=a466f6e749d887c596eeef2dd454bc93},
doi = {10.1007/978-3-319-07596-9_14},
issn = {21945357},
year  = {2014},
date = {2014-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {291},
pages = {127-134},
publisher = {Springer Verlag},
abstract = {The interoperability among the Health Information Systems is a natural demand nowadays. The Agency for Integration, Diffusion and Archive of Medical Information (AIDA) is a Multi-Agent System (MAS) specifically developed to guarantee interoperability in health organizations. This paper presents the Biomedical Multi-agent Platform for Interoperability (BMaPI) integrated in AIDA and it is used by all hospital services which communicates with AIDA, one of the examples is the Intensive Care Unit. The BMaPI main objective is to facilitate the communication among the agents of a MAS. It also assists the interaction between humans and agents through an interface that allows the administrators to create new agents easily and to monitor their activities in real time. Due to the BMaPI characteristics it is possible ensure the continuous work of the AIDA agents associated to INTCare system. The BMaPI was installed in Centro Hospitalar do Porto successfully, increasing the functionality and overall usability of AIDA platform. © Springer International Publishing Switzerland 2014.},
note = {cited By 10; Conference of 5th International Symposium on Ambient Intelligence, ISAmI 2014 ; Conference Date: 4 June 2014 Through 6 June 2014; Conference Code:116759},
keywords = {AIDA; Health information systems; Health organizations; Hospital service; INTCare; Medical information; Multi-agent platforms; Real time, Application programs; Artificial intelligence; Hospitals; Intelligent agents; Intensive care units; Interoperability; Medical information systems, Multi agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela2014165,
title = {Preventing patient cardiac arrhythmias by using data mining techniques},
author = {F. Portela and M. Filipe Santos and A. Silva and F. Rua and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925666219&doi=10.1109%2fIECBES.2014.7047478&partnerID=40&md5=251834d68395814ab7a32a47dfef6a59},
doi = {10.1109/IECBES.2014.7047478},
isbn = {9781479940844},
year  = {2014},
date = {2014-01-01},
journal = {IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet"},
pages = {165-170},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Cardiac Arrhythmia (CA) is very dangerous and can significantly undermine patient condition. New tools are fundamental to forecast and to prevent possible critical situations. In order to help clinicians acting proactively, predictive data mining real-time models were induced using online-learning. As input variables were considered those acquired at the patient admission and complementary variables (vital signs, laboratory results, therapeutics) hourly collected. The results are very motivating; sensitivity near to 95% was obtained when using Support Vector Machines. The approach explored in this work reveals to be an interesting contribution to the healthcare in terms of predicting CA and a good direction to be further explored. © 2014 IEEE.},
note = {cited By 18; Conference of 3rd IEEE Conference on Biomedical Engineering and Sciences, IECBES 2014 ; Conference Date: 8 December 2014 Through 10 December 2014; Conference Code:111205},
keywords = {Biomedical engineering; Diseases, Cardiac arrhythmia; Input variables; Online learning; Patient admissions; Patient condition; Predictive data mining; Real-time models; Vital sign, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Brandão2014379,
title = {Real-time Business Intelligence platform to maternity care},
author = {A. Brandão and E. Pereira and F. Portela and M. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925625301&doi=10.1109%2fIECBES.2014.7047525&partnerID=40&md5=c9c0dbde3a55cfa9f6d17b3d89ebf795},
doi = {10.1109/IECBES.2014.7047525},
isbn = {9781479940844},
year  = {2014},
date = {2014-01-01},
journal = {IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet"},
pages = {379-384},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The motivation for the implementation of a decision support system in maternity care came from the fact that people are constantly making quick decisions based on incomplete information. There is a significant impact on the patient health, as well as in increasing medical errors. To implement this system, it was resorted to the technologies of Business Intelligence, which involved the construction of two data warehouses with a dimensional structure in a star shape, for two distinct modules, in Gynecology and Obstetrics cares. The feasibility of an evidence-based practice and medical decision making in real time with universal and interoperable features are some of the benefits resulting from the implementation of decision support system in maternity care. In this paper we present the architecture of BI solution, some clinical outcomes and some benefits of the BI solution in a real world context. © 2014 IEEE.},
note = {cited By 10; Conference of 3rd IEEE Conference on Biomedical Engineering and Sciences, IECBES 2014 ; Conference Date: 8 December 2014 Through 10 December 2014; Conference Code:111205},
keywords = {Artificial intelligence; Behavioral research; Biomedical engineering; Competitive intelligence; Data warehouses; Decision making; Information analysis; Interoperability; Medicine, Clinical outcome; Dimensional structures; Evidence-based practices; Incomplete information; Medical decision making; Medical errors; Patient health; Real time business intelligence, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Abelha2014278,
title = {Simulating a multi-level priority triage system for maternity emergency},
author = {A. Abelha and E. Pereira and A. Brandão and F. Portela and M. Santos and J. Machado},
editor = {Tavares J. M. R. S. de Oliveira C.B. Brito A.C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922311084&partnerID=40&md5=ab182dda99e8716d8e62cdb08d6dd3b9},
isbn = {9789077381861},
year  = {2014},
date = {2014-01-01},
journal = {Modelling and Simulation 2014 - European Simulation and Modelling Conference, ESM 2014},
pages = {278-282},
publisher = {EUROSIS},
abstract = {Nowadays Decision Support Systems are increasingly used in order to help health professionals. An example of this application is the implementation of a triage system in hospital emergency. In Centro Materno Infantil do Norte it was implemented an intelligent pre-triage system aiming to prioritize the patients on two levels: Urgent and or outpatient service. However, although specific for obstetrics and gynaecology cases, the system does not meet all clinical requirements. Thus using a simulation algorithm developed within this framework, it was intended to simulate a specific priority triage system for gynaecology and obstetrics but with five acuity levels as suggested by the Portuguese general department of Health (Direção Geral de Saúde). For this study the repository of specific pre-tri age system was used to test the algorithm. After application it was found that the implementation of this system will reduce waiting time, allowing a uniform distribution according to the waiting time and the clinical features. The percentage of deviation between the waiting time and the actual time obtained by simulation algorithm it is approximately 121.6%.},
note = {cited By 3; Conference of 28th European Simulation and Modelling Conference, ESM 2014 ; Conference Date: 22 October 2014 Through 24 October 2014; Conference Code:110004},
keywords = {Artificial intelligence; Decision support systems; Gynecology; Modal analysis; Obstetrics; Real time systems, Department of healths; Health professionals; Intelligent decision support systems; Maternity care; Real time; Simulation algorithms; Specific priority triage system; Uniform distribution, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Rodrigues201421,
title = {Systematic coronary risk evaluation through artificial neural networks based systems},
author = {B. Rodrigues and S. Gomes and H. Vicente and A. Abelha and P. Novais and J. Machado and J. Neves},
editor = {Goto T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84913596405&partnerID=40&md5=ba1ff24b3257ceb3bfd80a72be19bee1},
isbn = {9781880843970},
year  = {2014},
date = {2014-01-01},
journal = {27th International Conference on Computer Applications in Industry and Engineering, CAINE 2014},
pages = {21-26},
publisher = {International Society of Computers and Their Applications (ISCA)},
abstract = {On the one hand, cardiovascular diseases have severe consequences on an individual and for the society in general, once they are the main cause to death. These facts reveal that it is vital to get preventive, by knowing how probable is to have that kind of illness. On the other hand, and until now, this risk has been assessed by a Systematic Coronary Risk Evaluation procedure that takes data from charts based on gender, age, total cholesterol, systolic blood pressure and smoking status, but with no conceivable potential to deal with the incomplete or default data that is presented on those tools. Therefore, the focus in this work will be on the development of a risk evaluation support system based on a low-risk record, grounded on a new approach to knowledge representation and reasoning, that based on an extension to the Logic Programming language, will be able to overcome the drawbacks of the present ones. This will be complemented with a computational framework based on Artificial Neural Networks. Copyright ISCA, CAINE 2014.},
note = {cited By 3; Conference of 27th International Conference on Computer Applications in Industry and Engineering, CAINE 2014 ; Conference Date: 13 October 2014 Through 15 October 2014; Conference Code:109020},
keywords = {Blood pressure; Computation theory; Knowledge representation; Logic programming; Neural networks, Cardio-vascular disease; Computational framework; Knowledge representation and reasoning; New approaches; Risk evaluation; Support systems; Systolic blood pressure; Total cholesterols, Risk assessment},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Veloso2014245,
title = {Real-time data mining models for predicting length of stay in Intensive Care Units},
author = {R. Veloso and F. Portela and M. F. Santos and Á. Silva and F. Rua and A. Abelha and J. Machado},
editor = {Filipe J. Filipe J. Liu K.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84909994261&doi=10.5220%2f0005083302450254&partnerID=40&md5=0a83bbca2a0b3c9c92b40d6707334e16},
doi = {10.5220/0005083302450254},
isbn = {9789897580505},
year  = {2014},
date = {2014-01-01},
journal = {KMIS 2014 - Proceedings of the International Conference on Knowledge Management and Information Sharing},
pages = {245-254},
publisher = {INSTICC Press},
abstract = {Nowadays the efficiency of costs and resources planning in hospitals embody a critical role in the management of these units. Length Of Stay (LOS) is a good metric when the goal is to decrease costs and to optimize resources. In Intensive Care Units (ICU) optimization assumes even a greater importance derived from the high costs associated to inpatients. This study presents two data mining approaches to predict LOS in an ICU. The first approach considered the admission variables and some other physiologic variables collected during the first 24 hours of inpatient. The second approach considered admission data and supplementary clinical data of the patient (vital signs and laboratory results) collected in real-time. The results achieved in the first approach are very poor (accuracy of 73 %). However, when the prediction is made using the data collected in real-time, the results are very interesting (sensitivity of 96.104%). The models induced in second experiment are sensitive to the patient clinical situation and can predict LOS according to the monitored variables. Models for predicting LOS at admission are not suited to the ICU particularities. Alternatively, they should be induced in real-time, using online-learning and considering the most recent patient condition when the model is induced.},
note = {cited By 11; Conference of 6th International Conference on Knowledge Management and Information Sharing, KMIS 2014 ; Conference Date: 21 October 2014 Through 24 October 2014; Conference Code:114703},
keywords = {Clinical situations; INTCare; Length of stay; Online learning; Patient condition; Real time; Real-time data mining; Resources planning, Data mining; Forecasting; Knowledge management, Intensive care units},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela201487,
title = {Pervasive and intelligent decision support in intensive medicine - The complete picture},
author = {F. Portela and M. F. Santos and J. MacHado and A. Abelha and Á. Silva and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906886530&doi=10.1007%2f978-3-319-10265-8_9&partnerID=40&md5=0e641a2fd40b48001fee00eaeea8f97d},
doi = {10.1007/978-3-319-10265-8_9},
issn = {03029743},
year  = {2014},
date = {2014-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {8649 LNCS},
pages = {87-102},
publisher = {Springer Verlag},
address = {Munich},
abstract = {In the Intensive Care Units (ICU) it is notorious the high number of data sources available. This situation brings more complexity to the way of how a professional makes a decision based on information provided by those data sources. Normally, the decisions are based on empirical knowledge and common sense. Often, they don't make use of the information provided by the ICU data sources, due to the difficulty in understanding them. To overcome these constraints an integrated and pervasive system called INTCare has been deployed. This paper is focused in presenting the system architecture and the knowledge obtained by each one of the decision modules: Patient Vital Signs, Critical Events, ICU Medical Scores and Ensemble Data Mining. This system is able to make hourly predictions in terms of organ failure and outcome. High values of sensitivity where reached, e.g. 97.95% for the cardiovascular system, 99.77% for the outcome. In addition, the system is prepared for tracking patients' critical events and for evaluating medical scores automatically and in real-time. © 2014 Springer International Publishing.},
note = {cited By 48; Conference of 5th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2014 ; Conference Date: 2 September 2014 Through 2 September 2014; Conference Code:107313},
keywords = {Cardiovascular system; Decision support systems; Information science, Critical events; Decision modules; Decision-based; Empirical knowledge; Intelligent decision support; Number of datum; Pervasive systems; System architectures, Intensive care units},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira2014352,
title = {Business intelligence in maternity care},
author = {E. Pereira and A. Brandöo and C. F. Portela and M. F. Santos and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906818146&doi=10.1145%2f2628194.2628248&partnerID=40&md5=c1e5a6f04bb641b8943f5bdfbaa05b96},
doi = {10.1145/2628194.2628248},
isbn = {9781450326278},
year  = {2014},
date = {2014-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {352-355},
publisher = {Association for Computing Machinery},
address = {Porto},
abstract = {The emergency services are usually pressured to make quick decisions with incomplete information on most cases, and this situation has a significant impact on healthcare as well on increasing medical errors. On the other hand, there has been an increase of the Electronic Health at Maternity Care. The combination of these two factors allows the construction of a Decision Support System specific for Maternity Care Unit using Business Intelligence technology. This solution is supported by a Data Warehouse, that uses the dimensional structure snowflake and makes the modeling of the maternity care database. With this solution it is intended to turn possible a clinical evidence-based practice, allowing for real time medical decision making with pervasive and interoperable characteristics. This paper presents the architecture, KPIs and benefits of Business Intelligence solution for the real context. This platform has several modules of clinical importance. The Obstetric Gynecological Emergency and the Voluntary Interruption of Pregnancy modules are object of study. This solution has an innovative contribution to the medical and scientific community studying the problem in Maternity area.© 2014 ACM.},
note = {cited By 7; Conference of 18th International Database Engineering and Applications Symposium, IDEAS 2014 ; Conference Date: 7 July 2014 Through 9 July 2014; Conference Code:107181},
keywords = {Artificial intelligence, Data warehouses; Decision support systems, Decision supports; Dimensional structures; Electronic health; Evidence-based practices; Incomplete information; Medical decision making; Medical errors; Scientific community},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marins2014197,
title = {Intelligent systems for monitoring and prevention in healthcare information systems},
author = {F. Marins and L. Cardoso and F. Portela and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904873936&doi=10.1007%2f978-3-319-09153-2_15&partnerID=40&md5=47cf42741b7034a2c531829c2f99a74c},
doi = {10.1007/978-3-319-09153-2_15},
issn = {03029743},
year  = {2014},
date = {2014-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {8584 LNCS},
number = {PART 6},
pages = {197-211},
publisher = {Springer Verlag},
address = {Guimaraes},
abstract = {Nowadays the interoperability in Healthcare Information Systems (HIS) is a fundamental requirement. The Agency for Integration, Diffusion and Archive of Medical Information (AIDA) is an interoperability healthcare platform that ensures these demands and it is implemented in Centro Hospitalar do Porto (CHP), a major healthcare unit in Portugal. Therefore, the overall performance of CHP HIS depends on the success of AIDA functioning. This paper presents monitoring and prevention systems implemented in the CHP, which aim to improve the system integrity and high availability. These systems allow the monitoring and the detection of situations conducive to failure in the AIDA main components: database, machines and intelligent agents. Through the monitoring systems, it was found that the database most critical period is between 11:00 and 12:00 and the resources are well balanced. The prevention systems detected abnormal situations that were reported to the administrators that took preventive actions, avoiding damage to AIDA workflow. © 2014 Springer International Publishing.},
note = {cited By 1; Conference of 14th International Conference on Computational Science and Its Applications, ICCSA 2014 ; Conference Date: 30 June 2014 Through 3 July 2014; Conference Code:106576},
keywords = {Availability; Damage detection; Health care; Information systems; Intelligent agents; Intelligent systems; Monitoring, Health care information system; Healthcare information systems (HIS); High availability; Medical information; Monitoring system; Prevention systems; Preventive action; System integrity, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marins2014207,
title = {cImproving high availability and reliability of health interoperability systems},
author = {F. Marins and L. Cardoso and F. Portela and M. F. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898613300&doi=10.1007%2f978-3-319-05948-8_20&partnerID=40&md5=f02681f3fd57aef2fc4d86a23731becc},
doi = {10.1007/978-3-319-05948-8_20},
issn = {21945357},
year  = {2014},
date = {2014-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {276 VOLUME 2},
pages = {207-216},
publisher = {Springer Verlag},
address = {Madeira},
abstract = {The accessibility and availability of patient clinical information are a constant need. The Agency for Interoperation, Diffusion and Archive of Medical Information (AIDA) was then developed to ensure the interoperability among healthcare information systems successfully. AIDA has demonstrated over time the need for greater control over its agents and their activities as the need for monitoring and preventing its machines and agents. This paper presents monitoring and prevention systems that were developed for machines and agents, which allow not only prevent faults, but also watch and evaluate the behaviour of these components through monitoring dashboards. The Biomedical Multiagent Platform for Interoperability (BMaPI) implemented in Centro Hospitalar do Porto (CHP) revealed provide the necessary data and functionalities capable to manage and to monitor agents' activities. It was found that the prevention systems identified critical situations successfully, contributing to an increase in the integrity and availability of AIDA implemented in CHP. © Springer International Publishing Switzerland 2014.},
note = {cited By 11; Conference of 2014 World Conference on Information Systems and Technologies, WorldCIST 2014 ; Conference Date: 15 April 2014 Through 18 April 2014; Conference Code:104563},
keywords = {Clinical information; Fault forecasting; Health care information system; Health information systems; Medical information; Monitoring system; Multi-agent platforms; Prevention systems, Information systems; Multi agent systems, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Oliveira2014407,
title = {Predictive models for hospital bed management using data mining techniques},
author = {S. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898611818&doi=10.1007%2f978-3-319-05948-8_39&partnerID=40&md5=3faf01ae721a500d77e3cfe0b5a19b89},
doi = {10.1007/978-3-319-05948-8_39},
issn = {21945357},
year  = {2014},
date = {2014-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {276 VOLUME 2},
pages = {407-416},
publisher = {Springer Verlag},
address = {Madeira},
abstract = {It is clear that the failures found in hospital management are usually related to the lack of information and insufficient resources management. The use of Data Mining (DM) can contribute to overcome these limitations in order to identify relevant data on patient's management and providing important information for managers to support their decisions. Throughout this study, were induced DM models capable to make predictions in a real environment using real data. For this, was adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. Three distinct techniques were considered: Decision Trees (DT), Naïve Bayes (NB) and Support Vector Machine (SVM) to perform classification tasks. This work explored the possibility to predict the number of patient discharges using only the number of discharges veirifed in the past. The models developed are able to predict the number of patient discharges per week with acuity values ranging from ≈82.69% to ≈94.23%. The use of these models can improve the efficiency of the administration of hospital beds. An accurate forecasting of discharges allows a better estimate of the beds available for the coming weeks. © Springer International Publishing Switzerland 2014.},
note = {cited By 6; Conference of 2014 World Conference on Information Systems and Technologies, WorldCIST 2014 ; Conference Date: 15 April 2014 Through 18 April 2014; Conference Code:104563},
keywords = {Classification tasks; CRISP-DM; Cross industry; Hospital management; Patient discharge; Predictive models; Real environments; Resources management, Data mining; Decision trees; Forecasting; Hospital beds; Hospitals; Information systems; Patient monitoring; Support vector machines, Information management},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marins201354,
title = {Intelligent information system to tracking patients in intensive care units},
author = {F. Marins and L. Cardoso and F. Portela and M. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893121287&doi=10.1007%2f978-3-319-03176-7_8&partnerID=40&md5=dda379011335db91cda96dbdb48ee95c},
doi = {10.1007/978-3-319-03176-7_8},
issn = {03029743},
year  = {2013},
date = {2013-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {8276 LNCS},
pages = {54-61},
address = {Carrillo},
abstract = {With the increasing expansion of health information systems, there is a need to create an interface: human, machine and the surrounding environment. This interface is called Ambient Intelligence and it has been increasing in the healthcare area. In this paper it is presented the Ambient Intelligence system implemented in the Intensive Care Unit of Centro Hospitalar do Porto, a hospital in the north of Portugal. This Ambient Intelligence is consisted by INTCare system, which the main goal is monitoring the patients' vital signs, PaLMS system, responsible for the patient's localisation and identification and AIDA, the platform that guarantees the interoperability from all information systems in the hospital. Furthermore, an usability evaluation was performed, described in this article, to find out what can be improved. © Springer International Publishing 2013.},
note = {cited By 4; Conference of 7th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2013 ; Conference Date: 2 December 2013 Through 6 December 2013; Conference Code:102301},
keywords = {Artificial intelligence; Information systems; Intensive care units; Medical computing; Ubiquitous computing; Information systems; Intelligent systems; Intensive care units; Medical computing, Interoperability; Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Aguiar2013177,
title = {Pervasive information systems to intensive care medicine: Technology acceptance model},
author = {J. Aguiar and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua and F. Pinto},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887660872&partnerID=40&md5=73d693941b90cb1efc126c47dc83d4c9},
isbn = {9789898565594},
year  = {2013},
date = {2013-01-01},
journal = {ICEIS 2013 - Proceedings of the 15th International Conference on Enterprise Information Systems},
volume = {1},
pages = {177-184},
address = {Angers},
abstract = {The usability of information systems in critical environments like Intensive Care Units (ICU) is far than the expected and desirable. Typically, ICUs have a set of not integrated information silos and a high number of data recorded in paper. Whenever ICU professionals need to make a decision they have to deal with a high number of data sources containing useful information. Unfortunately, they can't use those sources due to the difficulty of evaluating them in a correct time. Pervasive Intelligent Decision Support Systems (PIDSS), operating automatically and in real-time, can be used to improve the decision making if they are suited to the requirements of the ICU. In this work a PIDSS have been assessed in terms of quality and user acceptance making use of Technology Acceptance Model (TAM). TAM proved to be very useful when combined with Delphi method features to involve the professionals and to make the system usable.},
note = {cited By 5; Conference of 15th International Conference on Enterprise Information Systems, ICEIS 2013 ; Conference Date: 4 July 2013 Through 7 July 2013; Conference Code:100809},
keywords = {Artificial intelligence; Decision making; Decision support systems; Information systems, Critical environment; Delphi; Integrated informations; Intelligent decision support systems; Intensive care; Intensive care medicines; Pervasive information systems; Technology acceptance model, Intensive care units},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela20131,
title = {Pervasive and intelligent decision support in critical health care using ensembles},
author = {F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885207111&doi=10.1007%2f978-3-642-40093-3_1&partnerID=40&md5=5efa035b80b06da0c05d28fe4cbb2f44},
doi = {10.1007/978-3-642-40093-3_1},
issn = {03029743},
year  = {2013},
date = {2013-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {8060 LNCS},
pages = {1-16},
address = {Prague},
abstract = {Critical health care is one of the most difficult areas to make decisions. Every day new situations appear and doctors need to decide very quickly. Moreover, it is difficult to have an exact perception of the patient situation and a precise prediction on the future condition. The introduction of Intelligent Decision Support Systems (IDSS) in this area can help the doctors in the decision making process, giving them an important support based in new knowledge. Previous work has demonstrated that is possible to use data mining models to predict future situations of patients. Even so, two other problems arise: i) how fast; and ii) how accurate? To answer these questions, an ensemble strategy was experimented in the context of INTCare system, a pervasive IDSS to automatically predict the organ failure and the outcome of the patients throughout next 24 hours. This paper presents the results obtained combining real-time data processing with ensemble approach in the intensive care unit of the Centro Hospitalar do Porto, Porto, Portugal. © 2013 Springer-Verlag.},
note = {cited By 27; Conference of 4th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2013 ; Conference Date: 28 August 2013 Through 28 August 2013; Conference Code:99901},
keywords = {Artificial intelligence; Automobile drivers; Decision support systems; Forecasting; Health care; Information science; Intensive care units, Data mining models; Decision making process; Ensemble approaches; Ensemble strategies; Intelligent decision support; Intelligent decision support systems; Organ failure; Real-time data processing, Information technology},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela2013270,
title = {Data mining for real-time Intelligent Decision Support System in intensive care medicine},
author = {F. Portela and M. F. Santos and Á. Silva and J. Machado and A. Abelha and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877954083&partnerID=40&md5=2fe4f3dbeb72a99c6e8cfa657dc52383},
isbn = {9789898565389},
year  = {2013},
date = {2013-01-01},
journal = {ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence},
volume = {2},
pages = {270-276},
address = {Barcelona},
abstract = {The introduction of Intelligent Decision Support Systems (IDSS) in critical areas like Intensive Medicine is a complex and difficult process. The professionals of Intensive Care Units (ICU) haven't much time to register data because the direct care to the patients is always mandatory. In order to help doctors in the decision making process, the INTCare system has been deployed in the ICU of Centro Hospitalar of Porto, Portugal. INTCare is an IDSS that makes use of data mining models to predict the outcome and the organ failure probability for the ICU patients. This paper introduces the work carried out in order to automate the processes of data acquisition and data mining. The main goal of this work is to reduce significantly the manual efforts of the staff in the ICU. All the processes are autonomous and are executed in real-time. In particular, Decision Trees, Support Vector Machines and Naïve Bayes were used with online data to continuously adapt the predictive models. The data engineering process and achieved results, in terms of the performance attained, will be presented.},
note = {cited By 6; Conference of 5th International Conference on Agents and Artificial Intelligence, ICAART 2013 ; Conference Date: 15 February 2013 Through 18 February 2013; Conference Code:97005},
keywords = {Artificial intelligence; Automobile drivers; Data mining; Decision making; Decision support systems; Decision trees, Data engineering; Data mining models; Decision making process; Intelligent decision support systems; Intensive care medicines; Predictive models; Real time; Real-time Intelligent Decision Support Systems, Intensive care units},
pubstate = {published},
tppubtype = {inproceedings}
}
@inbook{Santos201387,
title = {Grid data mining strategies for outcome prediction in distributed intensive care units},
author = {M. F. Santos and F. Portela and M. Miranda and J. Machado and A. Abelha and A. Silva},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898257202&doi=10.4018%2f978-1-4666-3667-5.ch006&partnerID=40&md5=7a3d1bc440644ae630ef241a97c230a6},
doi = {10.4018/978-1-4666-3667-5.ch006},
isbn = {9781466636682; 146663667X; 9781466636675},
year  = {2013},
date = {2013-01-01},
journal = {Information Systems and Technologies for Enhancing Health and Social Care},
pages = {87-101},
publisher = {IGI Global},
abstract = {Previous work developed to predict the outcome of patients in the context of intensive care units brought to the light some requirements like the need to deal with distributed data sources. Those data sources can be used to induce local prediction models, and those models can in turn be used to induce global models more accurate and more general than the local models. This chapter introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. Five different tactics are explored for constructing the global model in a Distributed Data Mining (DDM) approach: Generalized Classifier Method (GCM), Specific Classifier Method (SCM), Weighed Classifier Method (WCM), Majority Voting Method (MVM), and Model Sampling Method (MSM). Experimental tests were conducted with a real world data set from intensive care medicine. The results demonstrate that the performance of DDM methods is very competitive when compared with the centralized methods. © 2013, IGI Global.},
note = {cited By 5},
keywords = {Data mining, Distributed data mining; Distributed data sources; Experimental test; Grid computing environment; Intensive care medicines; Learning classifier system; Local prediction; Outcome prediction, Forecasting; Grid computing; Intensive care units; Medical applications; Statistical tests},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Portela201355,
title = {Knowledge acquisition process for intelligent decision support in critical health care},
author = {F. Portela and A. Cabral and A. Abelha and M. Salazar and C. Quintas and J. Machado and J. Neves and M. F. Santos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898101050&doi=10.4018%2f978-1-4666-3667-5.ch004&partnerID=40&md5=af2425c3e5775e5d88d36e3e76372ffb},
doi = {10.4018/978-1-4666-3667-5.ch004},
isbn = {9781466636682; 146663667X; 9781466636675},
year  = {2013},
date = {2013-01-01},
journal = {Information Systems and Technologies for Enhancing Health and Social Care},
pages = {55-68},
publisher = {IGI Global},
abstract = {An efficient triage system is a good way to avoid some future problems and benefit the patient. However, a limitation still exists. The triage system is general and not specific to each case. Manchester Triage System is a reliable known system and is focused in the emergency department of a hospital. When applied to specific patients' conditions (such as pregnancy), it has several limitations. To overcome those limitations, an alternative triage system, integrated into an intelligent decision support system, was developed. The system classifies patients according to the severity of their clinical condition, establishing clinical priorities and not diagnosis. According to the urgency of attendance or problem type, it suggests one of three possible categories of the triage. This chapter presents the overall knowledge acquisition cycle associated with the workflow of patient arrival and the inherent decision making process. Results show that this new approach enhances the efficiency and the safety through the appropriate use of resources and by assisting the right patient in the right place, reducing the waiting triage time and the number in general urgency. © 2013, IGI Global.},
note = {cited By 23},
keywords = {Clinical conditions; Decision making process; Emergency departments; Intelligent decision support; Intelligent decision support systems; Manchester; New approaches; Patients' conditions, Decision making; Diagnosis; Hospitals; Knowledge acquisition, Decision support systems},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Portela2013410,
title = {Pervasive Ensemble Data Mining Models to Predict Organ Failure and Patient Outcome in Intensive Medicine},
author = {F. Portela and M. F. Santos and Á. Silva and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904295892&doi=10.1007%2f978-3-642-54105-6_27&partnerID=40&md5=b95b894d9b73bbb6bdc192f7fe27359f},
doi = {10.1007/978-3-642-54105-6_27},
issn = {18650929},
year  = {2013},
date = {2013-01-01},
journal = {Communications in Computer and Information Science},
volume = {415},
pages = {410-425},
publisher = {Springer Verlag},
address = {Barcelona},
abstract = {The number of patients admitted to Intensive Care Units with organ failure is significant. This type of situation is very common in Intensive Medicine. Intensive medicine is a specific area of medicine whose purpose is to avoid organ failure and recover patients in weak conditions. This type of problems can culminate in the death of patient. In order to help the intensive medicine professionals at the exact moment of decision making, a Pervasive Intelligent Decision Support System called INTCare was developed. INTCare uses ensemble data mining to predict the probability of occurring an organ failure or patient death for the next hour. To assure the better results, a measure was implemented to assess the models quality. The transforming process and model induction are both performed automatically and in real-time. The ensemble uses online-learning to improve the models. This paper explores the ensemble approach to improve the decision process in intensive Medicine. © Springer-Verlag Berlin Heidelberg 2013.},
note = {cited By 1; Conference of 4th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2012 ; Conference Date: 4 October 2012 Through 7 October 2012; Conference Code:106350},
keywords = {Data mining, Decision support systems; Intensive care units; Knowledge engineering; Knowledge management, Ensemble; INTCare; Intensive care; Organ failure; Patient Outcome; Pervasive healthcare; Real-time},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Portela20131b,
title = {Implementing a pervasive real-time intelligent system for tracking critical events with intensive care patients},
author = {F. Portela and P. Gago and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903153312&doi=10.4018%2fijhisi.2013100101&partnerID=40&md5=ddeb5aa9b2f510e714c7bb50d8123b62},
doi = {10.4018/ijhisi.2013100101},
issn = {15553396},
year  = {2013},
date = {2013-01-01},
journal = {International Journal of Healthcare Information Systems and Informatics},
volume = {8},
number = {4},
pages = {1-16},
publisher = {IGI Global},
abstract = {Nowadays, it is increasingly important to utilize intelligent systems to support the decision making process (DMP) in challenging areas such as Intensive Medicine. In Intensive Care Units (ICU), some of the biggest challenges relate both to the number and the different types of available data sources. Even though in such a setting the values for some variables are easy to collect, data collection is still performed manually in particular instances. In order to improve the DMP in ICU, a Pervasive Intelligent Decision Support System, called INTCare was deployed in the ICU of Centro Hospitalar do Porto in Portugal. This system altered the way information is collected and presented. Moreover, the tracking system deployed as a specific module of INTCare - Electronic Nursing Record (ENR) is made accessible anywhere and anytime. The system allows for the calculation of the critical events regarding fve variables that are typically monitored in an ICU. Specifically, the INTCare tracking system characterizes a grid that shows the events by type and duration, empowers a warning system to alert the doctors and promotes intuitive graphics that allow care providers to follow the patient care journey. User acceptance was measured through a questionnaire designed in accordance with the Technology Acceptance Model (TAM) and results of implementing the INTCare tracking system, and its interface are reported. Copyright © 2013, IGI Global.},
note = {cited By 11},
keywords = {Artificial intelligence; Decision making; Decision support systems; Intelligent systems; Nursing; Tracking (position), Critical events; Data collection; Decision-making process; Intelligent decision support systems; Intensive-care patients; Nursing records; Technology acceptance model; Tracking system, Intensive care units},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Pereira2013169,
title = {SWOT analysis of a Portuguese Electronic Health Record},
author = {R. Pereira and M. Salazar and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883047486&doi=10.1007%2f978-3-642-37437-1_14&partnerID=40&md5=a2a10063158ca1d8f53fce84dfd14e3e},
doi = {10.1007/978-3-642-37437-1_14},
issn = {18684238},
year  = {2013},
date = {2013-01-01},
journal = {IFIP Advances in Information and Communication Technology},
volume = {399},
pages = {169-177},
publisher = {Springer New York LLC},
address = {Athens},
abstract = {In this paper it is describe a SWOT analysis of an Electronic Heath Record (EHR) implemented in a Portuguese hospital. As the EHR is a core part of a hospital information system, it is extremely important to ensure that it offers the best functionalities and that users are satisfied. With this analysis it is intended to gather information about the system, in order to improve the EHR implemented in the hospital. In the end, and appending to the results of a usability evaluation done in previous works, the evaluation team had enough knowledge about are the strengths and weaknesses of the EHR, as well as what opportunities can be taken and the threats that have to be avoided. © IFIP International Federation for Information Processing 2013.},
note = {cited By 10; Conference of 12th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2013 ; Conference Date: 25 April 2013 Through 26 April 2013; Conference Code:98941},
keywords = {Core part; Electronic health record; Hospital information systems; SWOT analysis; Usability evaluation, eHealth, Electronic commerce; Hospitals; Records management},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pontes2013139,
title = {Modeling intelligent agents to integrate a patient monitoring system},
author = {G. Pontes and C. F. Portela and R. Rodrigues and M. F. Santos and J. Neves and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883036211&doi=10.1007%2f978-3-319-00563-8_17&partnerID=40&md5=ab0b5a4ebfbace91c91c0a7c76af113f},
doi = {10.1007/978-3-319-00563-8_17},
issn = {21945357},
year  = {2013},
date = {2013-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {221},
pages = {139-146},
publisher = {Springer Verlag},
address = {Salamanca},
abstract = {ICU units are a good environment for the application of intelligent systems in the healthcare arena, due to its critical environment that require diagnose, monitor and treatment of patients with serious illnesses. An intelligent decision support system - INTCare, was developed and tested in CHP, a hospital in Oporto, Portugal. The need to detect the presence or absence of the patient in bed, in order to stop the collection of redundant data concerning about the patient vital status led to the development of an RFID locating and monitoring system - PaLMS, able to uniquely and unambiguously identify a patient and perceive its presence in bed in an ubiquitous manner, making the process of data collection and alert event more accurate. An intelligent multi-agent system for integration of PaLMS in the hospital's platform for interoperability (AIDA) was also developed, using the characteristics of intelligent agents for the communication process between the RFID equipment, the INTCare module and the Patient Management System, using the HL7 standard embedded in agent behaviours. © Springer International Publishing Switzerland 2013.},
note = {cited By 5; Conference of 11th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2013 ; Conference Date: 22 May 2013 Through 24 May 2013; Conference Code:98887},
keywords = {Ambient intelligence; Communication process; Critical environment; Intelligent decision support systems; Intelligent multi agent systems; Medical informatics; Patient management; Patient monitoring systems, Communication; Decision support systems; Intelligent systems; Intensive care units; Interoperability; Multi agent systems; Patient monitoring; Radio frequency identification (RFID), Intelligent agents},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Salgado20139,
title = {Tracking People and Equipment Simulation inside Healthcare Units},
author = {C. Salgado and L. Cardoso and P. Gonçalves and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84882965823&doi=10.1007%2f978-3-319-00566-9_2&partnerID=40&md5=fe461106d775847023ee69c7d7fff500},
doi = {10.1007/978-3-319-00566-9_2},
issn = {21945357},
year  = {2013},
date = {2013-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {219},
pages = {9-16},
publisher = {Springer Verlag},
address = {Salamanca},
abstract = {Simulating the trajectory of a patient, health professional or medical equipment can have diverse advantages in a healthcare environment. Many hospitals choose and to rely on RFID tracking systems to avoid the theft or loss of equipment, reduce the time spent looking for equipment, finding missing patients or staff, and issuing warnings about personnel access to unauthorized areas. The ability to successfully simulate the trajectory of an entity is very important to replicate what happens in RFID embedded systems. Testing and optimizing in a simulated environment, which replicates actual conditions, prevent accidents that may occur in a real environment. Trajectory prediction is a software approach which provides, in real time, the set of sensors that can be deactivated to reduce power consumption and thereby increase the system's lifetime. Hence, the system proposed here aims to integrate the aforementioned strategies - simulation and prediction. It constitutes an intelligent tracking simulation system able to simulate and predict an entity's trajectory in an area fitted with RFID sensors. The system uses a Data Mining algorithm, designated SK-Means, to discover object movement patterns through historical trajectory data. © Springer International Publishing Switzerland 2013.},
note = {cited By 2; Conference of 4th International Symposium on Ambient Intelligence, ISAmI 2013 ; Conference Date: 22 May 2013 Through 24 May 2013; Conference Code:98888},
keywords = {Algorithms; Application programs; Artificial intelligence; Data mining; Equipment; Forecasting; Health care; Sensors; Target tracking, Data mining algorithm; Healthcare environments; Intelligent tracking; RFID object tracking; Simulated environment; Simulation; SK-Means; Trajectory prediction, Trajectories},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gonçalves2013201,
title = {Predict sepsis level in intensive medicine - Data mining approach},
author = {J. M. C. Gonçalves and F. Portela and M. F. Santos and Á. Silva and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876221428&doi=10.1007%2f978-3-642-36981-0_19&partnerID=40&md5=d5ca7bbcda25f0d1f2d0a30850f63245},
doi = {10.1007/978-3-642-36981-0_19},
issn = {21945357},
year  = {2013},
date = {2013-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {206 AISC},
pages = {201-211},
publisher = {Springer Verlag},
address = {Olhao, Algarve},
abstract = {This paper aims to support doctor's decision-making on predicting the Sepsis level. Thus, a set of Data Mining (DM) models were developed using prevision techniques and classification models. These models enable a better doctor's decision having into account the Sepsis level of the patient. The DM models use real data collected from the Intensive Care Unit of the Santo António Hospital, in Oporto, Portugal. Classification DM models were considered to predict sepsis level in a supervised learning approach. The models were induced making use of the following algorithms: Decision Trees, Support Vector Machines and Naïve Bayes classifier. The models were assessed using the Confusion Matrix, associated metrics, and Cross-validation. The analysis of the total error rate, sensitivity, specificity and accuracy were the metrics used to identify the most relevant measures to predict sepsis level. This work demonstrates that it is possible to predict with great accuracy the sepsis level. © 2013 Springer-Verlag.},
note = {cited By 12; Conference of 2013 World Conference on Information Systems and Technologies, WorldCIST 2013 ; Conference Date: 27 March 2013 Through 30 March 2013; Conference Code:96582},
keywords = {Classification (of information); Decision making; Decision trees; Forecasting; Information systems; Intensive care units, Classification models; Confusion matrices; Data mining models; INTCare; Intensive care; Sepsis; Supervised learning approaches; Total error rates, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Salazar2013685,
title = {Step towards paper free hospital through electronic health record},
author = {M. Salazar and J. Duarte and R. Pereira and F. Portela and M. F. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876207927&doi=10.1007%2f978-3-642-36981-0_63&partnerID=40&md5=e795b11607d69e53320b8cd85336b005},
doi = {10.1007/978-3-642-36981-0_63},
issn = {21945357},
year  = {2013},
date = {2013-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {206 AISC},
pages = {685-694},
publisher = {Springer Verlag},
address = {Olhao, Algarve},
abstract = {Information technology has great potential for transforming the health care system, improving quality of care. With the increasing expansion of health information systems, the Electronic Health Record (EHR) has become one of the finest sources for clinical information aggregators in the context of digital health. The EHR is a core part of a hospital information system, as well as a service on duty of the patient to improve the treatment of patients. It can be considered as a longitudinal electronic record of patient heath information, for example vital signs, medical history or laboratory data, generated by one or more encounters in any care delivery setting. As the EHR offers many potential opportunities for healthcare systems, it is important to take steps to improve the system. With this in mind, a study of the features present on a Portuguese EHR was made. The basis of this study was an adoption model that evaluates the EHR system accordingly to its current features. After this study, the EHR will be ranked into one of the existing eight stages. © 2013 Springer-Verlag.},
note = {cited By 7; Conference of 2013 World Conference on Information Systems and Technologies, WorldCIST 2013 ; Conference Date: 27 March 2013 Through 30 March 2013; Conference Code:96582},
keywords = {Clinical information; EHR; Electronic health record; EMRAM; Health information systems; Health-care system; HIMSS; Hospital information systems, Health care; Hospitals; Information systems, Records management},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Miranda201383,
title = {Healthcare interoperability through a JADE based multi-agent platform},
author = {M. Miranda and J. MacHado and A. Abelha and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867962333&doi=10.1007%2f978-3-642-32524-3_11&partnerID=40&md5=42c83d23e8d89d4eda904a889c765865},
doi = {10.1007/978-3-642-32524-3_11},
issn = {1860949X},
year  = {2013},
date = {2013-01-01},
journal = {Studies in Computational Intelligence},
volume = {446},
pages = {83-88},
publisher = {Springer Verlag},
abstract = {In the healthcare arena and in the design of its information systems, a fundamental principle is required for providing a better service throughout all departments, that principle is interoperability. Establishing connections among distinct service providers can be complicated and very often result in complex mesh of end-to-end flow of information which is too often hard to maintain and strongly coupled. Multi-agent system technology is a strong technology to address this subject. In this paper it is described an architecture of a multi-agent system, which aims to provide to the Health Information System with a distributed and consolidated tool towards implementing loosely-coupled interoperability among its systems.},
note = {cited By 8},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Peixoto2012264,
title = {Intelligence in interoperability with AIDA},
author = {H. Peixoto and M. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870862280&doi=10.1007%2f978-3-642-34624-8_31&partnerID=40&md5=b3c320834bc15085014dcd68b223c456},
doi = {10.1007/978-3-642-34624-8_31},
issn = {03029743},
year  = {2012},
date = {2012-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {7661 LNAI},
pages = {264-273},
address = {Macau},
abstract = {Healthcare systems have to be addressed in terms of a wide variety of heterogeneous, distributed and ubiquitous systems speaking different languages, integrating medical equipments and customized by different entities, which in turn were set by different people aiming at different goals. Demands of information within the healthcare sector range from clinically valuable patient-specific information to a variety of aggregation levels for follow-up and statistical and/or quantifiable reporting. The main goal is to gathering this information and present it in a readable way to physicians. In this work we show how to achieve interoperability in healthcare institutions using AIDA, an interoperability platform developed by researchers from the University of Minho and being used in some major Portuguese hospitals. © 2012 Springer-Verlag.},
note = {cited By 45; Conference of 20th International Symposium on Methodologies for Intelligent Systems, ISMIS 2012 ; Conference Date: 4 December 2012 Through 7 December 2012; Conference Code:94398},
keywords = {Aggregation level; Ambient intelligence; Electronic health record; Health-care system; Healthcare institutions; Healthcare sectors; Semantic interoperability; Ubiquitous systems, Health care; Intelligent systems; Semantics, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Rodrigues2012274,
title = {An intelligent patient monitoring system},
author = {R. Rodrigues and P. Gonçalves and M. Miranda and C. Portela and M. Santos and J. Neves and A. Abelha and J. MacHado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870862056&doi=10.1007%2f978-3-642-34624-8_32&partnerID=40&md5=3fd87effe75c4e219efb7b9b0b27e202},
doi = {10.1007/978-3-642-34624-8_32},
issn = {03029743},
year  = {2012},
date = {2012-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {7661 LNAI},
pages = {274-283},
address = {Macau},
abstract = {Intensive Care Units (ICUs) are a good environment for the application of intelligent systems in the healthcare arena, due to its critical environment that requires diagnose, monitoring and treatment of patients with serious illnesses. An intelligent decision support system - INTCare, was developed and tested in CHP (Centro Hospitalar do Porto), a hospital in Oporto, Portugal. The need to detect the presence or absence of the patient in bed, in order to stop the collection of redundant data concerning about the patient vital status led to the development of an RFID localisation and monitoring system - PaLMS, able to uniquely and unambiguously identify a patient and perceive its presence in bed in an ubiquitous manner, making the process of data collection and alert event more accurate. An intelligent multi-agent system for integration of PaLMS in the hospital's platform for interoperability (AIDA) was also developed, using the characteristics of intelligent agents for the communication process between the RFID equipment, the INTCare module and the Patient Management System (PMS), using the HL7 standard embedded in agent behaviours. © 2012 Springer-Verlag.},
note = {cited By 4; Conference of 20th International Symposium on Methodologies for Intelligent Systems, ISMIS 2012 ; Conference Date: 4 December 2012 Through 7 December 2012; Conference Code:94398},
keywords = {Ambient intelligence; HL7; Medical informatics; Multi agent system (MAS); Patient monitoring systems, Communication; Decision support systems; Health care; Intelligent agents; Intelligent systems; Intensive care units; Multi agent systems; Patient monitoring; Radio frequency identification (RFID), Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miranda2012949,
title = {Agent based interoperability in hospital information systems},
author = {M. Miranda and G. Pontes and A. Abelha and J. Neves and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886407426&doi=10.1109%2fBMEI.2012.6513042&partnerID=40&md5=ecc0808dff871cf40cc44103fa33628d},
doi = {10.1109/BMEI.2012.6513042},
isbn = {9781467311816},
year  = {2012},
date = {2012-01-01},
journal = {2012 5th International Conference on Biomedical Engineering and Informatics, BMEI 2012},
pages = {949-953},
address = {Chongqing},
abstract = {The healthcare area configures an environment of both complexity and cooperation. Numerous and distinct information systems must exchange information in a expedite and consolidated manner. Where healthcare interoperability is concerned numerous techniques, methodologies, architectures and standards exist, having also some which are more common. However subjects such as service distribution, fault tolerance, standards, communication flavoring and tightly-bound systems still are a major issue of concern. This paper aims to propose and explain a multi-agent based architecture which uses the HL7 standard as a means towards the implementation of interoperability in healthcare environment. It follows the concept of distributed consolidation of information, aiming heterogeneous systems to communicate towards their mutual benefit however through middleware agents which validate and consolidate information. © 2012 IEEE.},
note = {cited By 4; Conference of 2012 5th International Conference on Biomedical Engineering and Informatics, BMEI 2012 ; Conference Date: 16 October 2012 Through 18 October 2012; Conference Code:100337},
keywords = {Agent based; Healthcare environments; Healthcare Interoperability; Heterogeneous systems; Hospital information systems; Medical informatics; Mutual benefit; Service distribution, Health care; Information science; Information systems; Middleware; Multi agent systems, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva20121289,
title = {Step towards fault forecasting in hospital information systems},
author = {P. Silva and C. Quintas and M. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886397746&doi=10.1109%2fBMEI.2012.6513041&partnerID=40&md5=b12b2c0331dee2ea78784d8895bb0ea0},
doi = {10.1109/BMEI.2012.6513041},
isbn = {9781467311816},
year  = {2012},
date = {2012-01-01},
journal = {2012 5th International Conference on Biomedical Engineering and Informatics, BMEI 2012},
pages = {1289-1294},
address = {Chongqing},
abstract = {Nowadays, many organizations consider databases indispensable tools for their daily tasks. Particularly in healthcare units, databases have a vital role, since they archive very important information about patients' clinical status, therefore, it is crucial that databases are available twenty-four hours a day, seven days per week. Healthcare units have already implemented fault tolerant systems, which intended to ensure the availability, reliability and disaster recovery of data. However, these mechanisms do not allow to take preventive actions in order to avoid the occurrence of faults. In this context, the necessity of the development of faults prevention and prediction systems is emerging. These systems can predict faults with some time in advance and provide taking early action to solve problems. The objectives of this paper are: monitor database performance and adapt a forecasting model used in medicine (Modified Early Warning Score - MEWS) to database reality. © 2012 IEEE.},
note = {cited By 0; Conference of 2012 5th International Conference on Biomedical Engineering and Informatics, BMEI 2012 ; Conference Date: 16 October 2012 Through 18 October 2012; Conference Code:100337},
keywords = {Database performance; Early warning score; Fault tolerant systems; Forecasting modeling; Hospital information systems; Indispensable tools; Medical informatics; Prediction systems, Database systems, Forecasting; Health care; Hospitals; Information science; Information systems; Medicine},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira2012359,
title = {Usability evaluation of Electronic Health Record},
author = {R. Pereira and J. Duarte and M. Salazar and M. Santos and J. Neves and A. Abelha and J. MacHado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876779862&doi=10.1109%2fIECBES.2012.6498049&partnerID=40&md5=1b8dc2d261abbe67e912607d89d2af79},
doi = {10.1109/IECBES.2012.6498049},
isbn = {9781467316668},
year  = {2012},
date = {2012-01-01},
journal = {2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012},
pages = {359-364},
address = {Langkawi},
abstract = {Electronic Health Records (EHR) is a core part of a hospital information system, as well as a service on duty of the patient to improve the treatment of patients. It can be considered as a longitudinal electronic record of patient heath information, for example vital signs, medical history or laboratory data, generated by one or more encounters in any care delivery setting. Although the great advantages that an EHR system provides to an hospital, its adoption is slow to date due to a fundamental failure on the usability and the associated costs. This paper intends to present a usability evaluation to the EHR of a Portuguese hospital. It discusses how an inspection usability method, Heuristic Walkthrough, was used to evaluate and improve the usability of the EHR system. Outcomes from the evaluation resulted in understanding what can be improved to achieve a better system. Understand the level of usability presented by the EHR was other of the main objectives. © 2012 IEEE.},
note = {cited By 6; Conference of 2012 2nd IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012 ; Conference Date: 17 December 2012 Through 19 December 2012; Conference Code:96763},
keywords = {Associated costs; Delivery settings; Electronic health record; Electronic records; Heuristic Walkthrough; Hospital information systems; Usability evaluation; Usability methods, Biomedical engineering; Hospitals; Records management, Usability engineering},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva201263,
title = {Hospital database workload and fault forecasting},
author = {P. Silva and C. Quintas and J. Duarte and M. Santos and J. Neves and A. Abelha and J. MacHado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876746497&doi=10.1109%2fIECBES.2012.6498150&partnerID=40&md5=35fa2df9982add8528a17ca52619208a},
doi = {10.1109/IECBES.2012.6498150},
isbn = {9781467316668},
year  = {2012},
date = {2012-01-01},
journal = {2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012},
pages = {63-68},
address = {Langkawi},
abstract = {With the growing importance of hospital information systems, databases became indispensable tools for day-to-day tasks in healthcare units. They store important and confidential information about patients clinical status and about the other hospital services. Thus, they must be permanently available, reliable and at high performance. In many healthcare units, fault tolerant systems are used. They ensure the availability, reliability and disaster recovery of data. However, these mechanisms do not allow the prediction or prevention of faults. In this context, it emerges the necessity of developing a fault forecasting system. The objectives of this paper are monitoring database performance to verify the normal workload for the main database of Centro Hospitalar do Porto and adapt a forecasting model used in medicine into the database context. Based on percentiles it was created a scale to represent the severity of situations. It was observe that the critical workload period is the period between 10:00 am and 12:00 am. Moreover, abnormal situations were detected and it was possible to send alerts and to request assistance. © 2012 IEEE.},
note = {cited By 6; Conference of 2012 2nd IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012 ; Conference Date: 17 December 2012 Through 19 December 2012; Conference Code:96763},
keywords = {Confidential information; Database performance; Database workload; Disaster recovery; Fault tolerant systems; Forecasting models; Hospital information systems; Indispensable tools, Database systems, Forecasting; Health care; Hospitals; Medical computing; Medicine},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Vicente2012446,
title = {Prediction of the quality of public water supply using artificial neural networks},
author = {H. Vicente and S. Dias and A. Fernandes and A. Abelha and J. MacHado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872189812&doi=10.2166%2faqua.2012.014&partnerID=40&md5=21439d46a149b88ee4c82a7bee0e14ce},
doi = {10.2166/aqua.2012.014},
issn = {00037214},
year  = {2012},
date = {2012-01-01},
journal = {Journal of Water Supply: Research and Technology - AQUA},
volume = {61},
number = {7},
pages = {446-459},
abstract = {The Health Surveillance Program was established by the Regional Health Authority of Alentejo to control the quality of public water supply. This authority divides the water quality parameters into three distinct groups, namely P1(pH and conductivity), P2 (nitrate and manganese) and P3 (sodium and potassium), for which the sampling frequency is dissimilar. Thus, the development of formal models is essential to predict the chemical parameters included in group P2 and included in group P 3, for which the sampling frequency is lower, based on the chemical parameters included in group P1. In the present work, artificial neural networks (ANNs) were used to predict the concentration of nitrate, manganese, sodium and potassium from pH and conductivity. Different network structures have been elaborated and evaluated using the mean absolute deviation and the mean squared error. The ANN selected to predict the concentration of nitrate, sodium and potassium from pH and conductivity has a 2-18-14-3 topology while the network selected to predict the concentration of nitrate and manganese has a 2-19-10-2 topology. A good match between the observed and predicted values was observed with the R2 values varying in the range 0.9960-0.9989 for the training set and 0.9993-0.9952 for the test set. © IWA Publishing 2012.},
note = {cited By 38},
keywords = {Alentejo; Portugal, artificial neural network; concentration (composition); monitoring; numerical model; physicochemical property; prediction; water quality; water supply, Chemical parameters; Formal model; Mean absolute deviations; Mean squared error; Network structures; Public water supply; Sampling frequencies; Surveillance program; Test sets; Training sets; Water quality parameters, Forecasting; Manganese; Neural networks; Nitrates; pH; Potassium; Sodium; Topology; Water quality; Water supply, Quality control},
pubstate = {published},
tppubtype = {article}
}
@article{Couto2012300,
title = {Water quality modeling using artificial intelligence-based tools},
author = {C. Couto and H. Vicente and J. Machado and A. Abelha and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866094661&doi=10.2495%2fDNE-V7-N3-300-309&partnerID=40&md5=3370d0fa5d0627d8a4cf411cf1d19814},
doi = {10.2495/DNE-V7-N3-300-309},
issn = {17557437},
year  = {2012},
date = {2012-01-01},
journal = {International Journal of Design and Nature and Ecodynamics},
volume = {7},
number = {3},
pages = {300-309},
abstract = {Water, like any other biosphere natural resource, is scarce, and its judicious use includes its quality safeguarding. Indeed, there is a wide concern to the fact that an ineffi cient water management system may become one of the major drawbacks for a human-centered sustainable development process. The assessment of reservoir water quality is constrained due to geographic considerations, the number of parameters to be considered and the huge fi nancial resources needed to get such data. Under these circumstances, the modeling of water quality in reservoirs is essential in the resolution of environmental problems and has lately been asserting itself as a relevant tool for a sustainable and harmonious progress of the populations. The analysis and development of forecast models, based on Artifi cial Intelligence-based tools and the new methodologies for problem solving, has proven to be an alternative, having in mind a pro-active behavior that may contribute decisively to diagnose, preserve, and rehabilitate the reservoirs. In particular, this work describes the training, validation and application of Artifi cial Neural Networks (ANNs) and Decision Trees (DTs) to forecast the water quality of the Odivelas reservoir, in Portugal, over a period of 10 years. The input variables of the ANN model are chemical oxygen demand (COD), dissolved oxygen (DO), and oxidability and total suspended solids (TSS), while for the DT the inputs are, in addition to those used by ANN, the Water Conductivity and the Temperature. The performance of the models, evaluated in terms of the coincidence matrix, created by matching the predicted and actual values, are very similar for both models; the percentage of adjustments relative to the number of presented cases is 98.8% for the training set and 97.4% for the testing one. © 2012 WIT Press.},
note = {cited By 10},
keywords = {Artificial Intelligence; Chemical Oxygen Demand; Data Processing; Decision Theory; Mathematical Models; Neural Networks; Reservoirs; Water Management; Water Quality, Chemical oxygen demand; Data mining; Decision trees; Forestry; Neural networks; Neurons; Reservoirs (water); Water management, Coincidence matrixes; Decision trees (DTs); Environmental problems; FORECAST model; Input variables; Neuronal networks; Portugal; Reservoir water quality; Total suspended solids; Training sets; Water conductivity; Water management systems; Water quality modeling, Water quality},
pubstate = {published},
tppubtype = {article}
}
@article{Vicente2012310,
title = {Prediction of water quality parameters in a reservoir using artificial neural networks},
author = {H. Vicente and C. Couto and J. Machado and A. Abelha and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866088685&doi=10.2495%2fDNE-V7-N3-310-319&partnerID=40&md5=1f182745896b8b03236cd067ca5a219e},
doi = {10.2495/DNE-V7-N3-310-319},
issn = {17557437},
year  = {2012},
date = {2012-01-01},
journal = {International Journal of Design and Nature and Ecodynamics},
volume = {7},
number = {3},
pages = {310-319},
abstract = {Water quality brings to the ground the discussion on water utilization once the consumption, of degraded water, is not possible or safe. On the other hand, the assessment of the water quality in a reservoir is constrained due to geographic considerations, the number of parameters to be studied, and the huge fi nancial resources needed to get the necessary data. To this picture it should be added the latency times between the sampling moment and the instant that portrait the results of the laboratory analyses. However, new approaches to problem solving, namely those borrowed from the Artifi cial Intelligence arena have proven their ability and applicability in terms of simulation and modeling of the physical phenomena. Indeed, Artifi cial Neural Networks (ANNs) capture the embedded spatial and unsteady behavior in the investigated problem, using its architecture and nonlinearity nature, when compared with the other classical modeling techniques. This work describes the training, validation, and application of ANNs models for computing the oxidability and total suspended solids (TSS) levels in the Monte Novo reservoir, in Portugal, over a period of 15 years. Different network structures have been elaborated and evaluated. The performance of the ANNs models was assessed through the coeffi cient of determination (R2), mean absolute deviation, mean squared error, and bias computed from the measured and model calculated values of the dependent variables. Goodness of the model fi t to the data was also evaluated through the relationship between the errors and model computed values of oxidability and TSS. The ANNs selected to predict the oxidability from pH, conductivity, dissolved oxygen (DO), water temperature, and volume of water stored in reservoir has a 4-11-5-1 topology, while the network selected to predict the TSS has a 5-6-5-1 topology. A good match between the observed and predicted values was observed with the R2 values varying in the range 0.995-0.998 for the training set, and 0.994-0.996 for the test set. © 2012 WIT Press.},
note = {cited By 13},
keywords = {Biochemical oxygen demand; Forecasting; Models; Neural networks; Topology; Water quality, Reservoirs (water)},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Portela20121,
title = {Intelligent data acquisition and scoring system for intensive medicine},
author = {F. Portela and M. F. Santos and J. Machado and Á. Silva and F. Rua and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84865601812&doi=10.1007%2f978-3-642-32395-9_1&partnerID=40&md5=37afeed62d344c257e381436d36ca837},
doi = {10.1007/978-3-642-32395-9_1},
issn = {03029743},
year  = {2012},
date = {2012-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {7451 LNCS},
pages = {1-15},
address = {Vienna},
abstract = {In a critical area as is Intensive Medicine, the existence of systems to support the clinical decision is mandatory. These systems should ensure a set of data to evaluate medical scores like is SAPS, SOFA and GLASGOW. The value of these scores gives the doctors the ability to understand the real condition of the patient and provides a mean to improve their decisions in order to choose the best therapy for the patient. Unfortunately, almost all of the required data to obtain these scores are recorded on paper and rarely are stored electronically. Doctors recognize this as an important limitation in the Intensive Care Units. This paper presents an intelligent system to obtain the data, calculate the scores and disseminate the results in an online, automatic, continuous and pervasive way. The major features of the system are detailed and discussed. A preliminary assessment of the system is also provided. © 2012 Springer-Verlag.},
note = {cited By 9; Conference of 3rd International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2012 ; Conference Date: 4 September 2012 Through 5 September 2012; Conference Code:92338},
keywords = {GLASGOW; Intensive Medicine Scores; Pervasive systems; Real time; SAPS; Scoring systems; SOFA, Information science; Intelligent systems; Intensive care units; Patient treatment, Information technology},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Neves2012119,
title = {Evolutionary intelligence in asphalt pavement modeling and quality-of-information},
author = {J. Neves and J. Ribeiro and P. Pereira and V. Alves and J. Machado and A. Abelha and P. Novais and C. Analide and M. Santos and M. Fernández-Delgado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879738869&doi=10.1007%2fs13748-011-0003-5&partnerID=40&md5=f8284dad49fe5e6e9e81616db5854595},
doi = {10.1007/s13748-011-0003-5},
issn = {21926352},
year  = {2012},
date = {2012-01-01},
journal = {Progress in Artificial Intelligence},
volume = {1},
number = {1},
pages = {119-135},
publisher = {Springer Verlag},
abstract = {The analysis and development of a novel approach to asphalt pavement modeling, able to attend the need to predict the failure according to technical and non-technical criteria in a highway, is a hard task, namely in terms of the huge amount of possible scenarios. Indeed, the current state-of-the-art for service-life prediction is at empiric and empiric-mechanistic levels, and does not provide any suitable answer even for a single failure criteria. Consequently, it is imperative to achieve qualified models and qualitative reasoning methods, in particular due to the need to have first-class environments at our disposal where defective information is at hand. To fulfill this goal, this paper presents a dynamic and formal model oriented to fulfill the task of making predictions for multi-failure criteria, in particular in scenarios with incomplete information; it is an intelligence tool that advances according to the quality-of-information of the extensions of the predicates that model the universe of discourse. On the other hand, it is also considered the degree-of-confidence factor, a parameter that measures one's confidence on the list of characteristics presented by an asphalt pavement, set in terms of the attributes or variables that make the argument of the predicates referred to above. © 2011 Springer-Verlag.},
note = {cited By 14},
keywords = {Asphalt mixtures, Asphalt pavements; Failure (mechanical); Forecasting; Knowledge representation; Logic programming, Degree of confidence; Evolutionary intelligence; Extended logic programming; Knowledge representation and reasoning; Quality of information},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Rodrigues2012851,
title = {Monitoring intelligent system for the Intensive Care Unit using RFID and multi-agent systems},
author = {R. Rodrigues and P. Gonçalves and M. Miranda and F. Portela and M. Santos and J. Neves and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903839575&doi=10.1109%2fIEEM.2012.6837860&partnerID=40&md5=be7c692d65ed1bb3e473117cb6ab9fc1},
doi = {10.1109/IEEM.2012.6837860},
issn = {21573611},
year  = {2012},
date = {2012-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
pages = {851-855},
publisher = {IEEE Computer Society},
address = {Hong Kong},
abstract = {In an environment where patients' lives are at stake, Intensive Care Units (ICUs) become a good scenario for the implementation of Ambient Intelligence, helping medical professionals in their task of retrieving the well-being to patients. INTCare project is a system that aims the real-time monitoring of patients, and predicts their outcome in a short period of time. When patients' vital signs get out of range, an alert system warns medical staff about the patient condition. PaLMS, a Patient Localization and Monitoring System, is being developed and tested in Centro Hospitalar do Porto - CHP, a hospital in Portugal. It uses RFID technology with a multi-agent architecture for communications inside hospital, thus providing a way to improve INTCare by ending the storage and analysis of redundant data, collected when the patient isn't in the bed, plus stopping the warning events triggered by the vital signs out of range. © 2012 IEEE.},
note = {cited By 11; Conference of 2012 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2012 ; Conference Date: 10 December 2012 Through 13 December 2012; Conference Code:106121},
keywords = {Ambient intelligence; Medical professionals; Monitoring system; Multiagent architecture; Patient condition; Patient localization; Real time monitoring; RFID Technology, Digital storage; Industrial engineering; Intelligent systems; Internet of things; Multi agent systems, Intensive care units},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pereira20121568,
title = {Usability of an electronic health record},
author = {R. Pereira and J. Duarte and M. Salazar and M. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903838002&doi=10.1109%2fIEEM.2012.6838010&partnerID=40&md5=fdb0000daa2f246f4c1e01423f80f1a7},
doi = {10.1109/IEEM.2012.6838010},
issn = {21573611},
year  = {2012},
date = {2012-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
pages = {1568-1572},
publisher = {IEEE Computer Society},
address = {Hong Kong},
abstract = {The Electronic Health Records (EHR) is a longitudinal electronic record of patient heath information generated by one or more encounters in any care delivery setting. The information included in the type of records can be progress notes, problems, medication, vital signs, medical history, laboratory data, radiology reports and much more. The usability of an EHR is crucial to achieve success, as well as guaranteeing a high level of safe and effective level of patient care system. Therefore, it is important for care providers to take steps to ensure that their EHR will be usable. Aiming to assess the usability of the EHR, a usability evaluation was performed. This paper describes how two usability evaluation methods (heuristic walkthrough and surveys) were used to evaluate its usability. With those evaluations, it was able to recognize the level of usability present in the EHR, along with the usability issues that can be disposed. © 2012 IEEE.},
note = {cited By 15; Conference of 2012 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2012 ; Conference Date: 10 December 2012 Through 13 December 2012; Conference Code:106121},
keywords = {Delivery settings; Electronic health record; Electronic records; Heuristic Walkthrough; Laboratory datum; Radiology reports; Usability evaluation; Usability evaluation methods, Industrial engineering; Medical problems; Records management, Usability engineering},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Silva2012846,
title = {Intelligent systems based in hospital database malfunction scenarios},
author = {P. Silva and C. Quintas and P. Goncalves and G. Pontes and M. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903826869&doi=10.1109%2fIEEM.2012.6837859&partnerID=40&md5=941aed69ef0d4695f9541c1b9e0059cb},
doi = {10.1109/IEEM.2012.6837859},
issn = {21573611},
year  = {2012},
date = {2012-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
pages = {846-850},
publisher = {IEEE Computer Society},
address = {Hong Kong},
abstract = {Databases are indispensable for everyday tasks in organizations, particularly in healthcare units. Databases archive important and confidential information about patient's clinical status. Therefore, they must always be available, reliable and at high performance level. In healthcare units, fault tolerant systems ensure the availability, reliability and disaster recovery of data. However, these mechanisms do not allow taking preventive actions in order to avoid fault occurrence. In this context, it emerges the necessity of developing a fault prevention system. It can predict database malfunction in advance and provides early decision taken to solve problems. The objectives of this paper are: monitoring database performance and adapt a forecasting model used in medicine (MEWS) to the database context. Based on mathematical tools it was created a scale that assesses the severity of abnormal situations. In this way, it is possible to define the scenarios where database symptoms must trigger alerts and assistance request. © 2012 IEEE.},
note = {cited By 1; Conference of 2012 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2012 ; Conference Date: 10 December 2012 Through 13 December 2012; Conference Code:106121},
keywords = {Confidential information; Database performance; Disaster recovery; Fault tolerant systems; Forecasting modeling; Mathematical tools; Performance level; Preventive action, Database systems, Health care; Industrial engineering; Intelligent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Viana2012251,
title = {Step towards simulation and monitoring of hospital waiting lists},
author = {M. Viana and O. Oliveira and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899008754&partnerID=40&md5=26ef3a69986fd26fcfd230dcf0809614},
year  = {2012},
date = {2012-01-01},
journal = {ESM 2012 - 2012 European Simulation and Modelling Conference: Modelling and Simulation 2012},
pages = {251-254},
publisher = {EUROSIS},
address = {Essen},
abstract = {Nowadays, the key to competitive advantage is being able to identify, summarize and categorize data. Currently, organizations should be able to interpret and convert information in a differentiating factor for those who are responsible for decision-making can take advantage of it. The main aim of this paper is to simulate and monitor real clinical data from Hospital Geral Santo Antonio (HGSA) in order to find trends and indicators that can support decision-making. It was made some experiments in ICU arena and in the hospital waiting lists (surgery and appointment). In the experimental phase it was used the open source BI tool Pentaho Suite in order to proceed the Knowledge Discovery (KD) process. It was considered an efficient process to clinical data simulation and monitoring, such as Pentaho BI tool. © 2012 EUROSIS-ETI.},
note = {cited By 1; Conference of 26th European Simulation and Modelling Conference, ESM 2012 ; Conference Date: 22 October 2012 Through 24 October 2012; Conference Code:104382},
keywords = {Clinical data; Competitive advantage; Efficient process; ETL process; Open source tools; Open sources; Simulation and monitoring; Waiting lists, Competition; Competitive intelligence; Data mining; Data warehouses; Decision making; Modal analysis; Tools, Hospitals},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela2012260,
title = {Intelligent decision support in intensive care - Towards technology acceptance},
author = {F. Portela and M. F. Santos and J. Machado and A. Abelha and J. Neves and A. Silva and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898957706&partnerID=40&md5=e0837d4a8fea6e393845366adc7f0e47},
year  = {2012},
date = {2012-01-01},
journal = {ESM 2012 - 2012 European Simulation and Modelling Conference: Modelling and Simulation 2012},
pages = {260-266},
publisher = {EUROSIS},
address = {Essen},
abstract = {Decision support technology acceptance is a critical factor in the success of the adoption this type of systems by the users. INTCARE is an intelligent decision support system for intensive care medicine. The main purpose of this system is to help the doctors and nurses making decisions more proactively based on the prediction of the organ failure and the outcome of the patients. To assure the adoption of INTCARE by the doctors and by the nurses, several requirements had taken into account: process dematerialization (information is now in electronic format); interoperability among the systems (the AIDA platform was used to inter operate with other information systems); online data acquisition and real-time processing (a set of software agents has been developed to accomplish these tasks). A technology acceptance methodology has been followed in the Intensive Care Unit (ICU) of Centro Hospitalar do Porto in order to assure the most perfect alignment between the functional and technical characteristics of INTCARE and the user expectations. Results showed that the ICU staff is permeable to the system. In general more than 90 % of the answers are scored with 4 or 5 points which gives a good motivation to continue the work. © 2012 EUROSIS-ETI.},
note = {cited By 4; Conference of 26th European Simulation and Modelling Conference, ESM 2012 ; Conference Date: 22 October 2012 Through 24 October 2012; Conference Code:104382},
keywords = {Artificial intelligence; Decision support systems; Interoperability; Modal analysis; Nursing; Technology, INTCARE; Intelligent decision support systems; Intensive care medicines; Real-time; Technology acceptance, Intensive care units},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela2011241,
title = {Knowledge discovery for pervasive and real-time Intelligent Decision Support in intensive care medicine},
author = {F. Portela and P. Gago and M. F. Santos and A. Silva and F. Rua and J. MacHado and A. Abelha and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862220885&partnerID=40&md5=9688d0b5f7c8631d81c5b237252e080b},
isbn = {9789898425812},
year  = {2011},
date = {2011-01-01},
journal = {KMIS 2011 - Proceedings of the International Conference on Knowledge Management and Information Sharing},
pages = {241-249},
address = {Paris},
abstract = {Pervasiveness, real-time and online processing are important requirements included in the researchers' agenda for the development of future generation of Intelligent Decision Support Systems (IDSS). In particular, knowledge discovery based IDSS operating in critical environments such of intensive care, should be adapted to those new requests. This paper introduces the way how INTCare, an IDSS developed in the intensive care unit of the Centro Hospitalar do Porto, will accommodate the new functionalities. Solutions are proposed for the most important constraints, e.g., paper based data, missing values, values out-of-range, data integration, data quality. The benefits and limitations of the approach are discussed.},
note = {cited By 27; Conference of International Conference on Knowledge Management and Information Sharing, KMIS 2011 ; Conference Date: 26 October 2011 Through 29 October 2011; Conference Code:90186},
keywords = {Artificial intelligence; Data acquisition; Decision support systems; Information analysis; Intensive care units; Knowledge management, Automobile drivers, Clinical data; Decision support process; Intensive care medicines; Pervasive; Real-time},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Cabral2011223,
title = {Data acquisition process for an intelligent decision support in gynecology and obstetrics emergency triage},
author = {A. Cabral and C. Pina and H. Machado and A. Abelha and M. Salazar and C. Quintas and C. F. Portela and J. Machado and J. Neves and M. F. Santos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80054072367&doi=10.1007%2f978-3-642-24352-3_24&partnerID=40&md5=cc683ed622228453b4999c2a17a96a86},
doi = {10.1007/978-3-642-24352-3_24},
issn = {18650929},
year  = {2011},
date = {2011-01-01},
journal = {Communications in Computer and Information Science},
volume = {221 CCIS},
number = {PART 3},
pages = {223-232},
address = {Vilamoura},
abstract = {Manchester Triage System is a reliable system of triage in the emergency department of a hospital. This system when applied to a specific patients' condition such the pregnancy has several limitations. To overcome those limitations an alternative triage IDSS was developed in the MJD. In this approach the knowledge was obtained directly from the doctors' empirical and scientific experience to make the first version of decision models. Due to the particular gynecological and/or obstetrics requests other characteristics had been developed, namely a system that can increase patient safety for women in need of immediate care and help low-risk women avoid high-risk care, maximizing the use of resources. This paper presents the arrival flowchart, the associated decisions and the knowledge acquisition cycle. Results showed that this new approach enhances the efficiency and the safety through the appropriate use of resources and by assisting the right patient in the right place. © 2011 Springer-Verlag.},
note = {cited By 9; Conference of International Conference on Enterprise Information Systems, CENTERIS 2011 ; Conference Date: 5 October 2011 Through 7 October 2011; Conference Code:86917},
keywords = {Acquisition process; Decision models; Emergency departments; Intelligent decision support; Intelligent decision support systems; Manchester; Patient safety; Reliable systems; Triage, Artificial intelligence; Decision support systems; Emergency rooms; Knowledge acquisition; Obstetrics, Information systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Duarte2011156,
title = {Electronic health record in dermatology service},
author = {J. Duarte and C. F. Portela and A. Abelha and J. Machado and M. F. Santos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80054069443&doi=10.1007%2f978-3-642-24352-3_17&partnerID=40&md5=6f89343e478430a9f1a1927a52873d31},
doi = {10.1007/978-3-642-24352-3_17},
issn = {18650929},
year  = {2011},
date = {2011-01-01},
journal = {Communications in Computer and Information Science},
volume = {221 CCIS},
number = {PART 3},
pages = {156-164},
address = {Vilamoura},
abstract = {In this paper we describe the implementation of an Electronic Health Record in the Dermatology service of a Portuguese hospital. This system must follow the principle of simplicity, enabling recording quality and analytical processing. Standards and norms were also followed and it is shown that interoperability has a key role in the whole process. This project is a good example of cooperation between academic and healthcare institutions and shows the impact of new technology on healthcare organizations. © 2011 Springer-Verlag.},
note = {cited By 27; Conference of International Conference on Enterprise Information Systems, CENTERIS 2011 ; Conference Date: 5 October 2011 Through 7 October 2011; Conference Code:86917},
keywords = {Dermatology; Health; Health care; Interoperability; Records management, Electronic health record; Health records; Healthcare institutions; Healthcare organizations; Whole process, Information systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela2011233,
title = {Enabling a pervasive approach for intelligent decision support in critical health care},
author = {C. F. Portela and M. F. Santos and Á. Silva and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80054063830&doi=10.1007%2f978-3-642-24352-3_25&partnerID=40&md5=530df95e3ad02b1a1ef2baf4771f8385},
doi = {10.1007/978-3-642-24352-3_25},
issn = {18650929},
year  = {2011},
date = {2011-01-01},
journal = {Communications in Computer and Information Science},
volume = {221 CCIS},
number = {PART 3},
pages = {233-243},
address = {Vilamoura},
abstract = {The creation of a pervasive and intelligent environment makes possible the remote work with good results in a great range of applications. However, the critical health care is one of the most difficult areas to implement it. In particular Intensive Care Units represent a new challenge for this field, bringing new requirements and demanding for new features that should be satisfied if we want to succeed. This paper presents a framework to evaluate future developments in order to efficiently adapt an Intelligent Decision Support System to a pervasive approach in the area of critical health (INTCare research project). © 2011 Springer-Verlag.},
note = {cited By 31; Conference of International Conference on Enterprise Information Systems, CENTERIS 2011 ; Conference Date: 5 October 2011 Through 7 October 2011; Conference Code:86917},
keywords = {Artificial intelligence; Decision support systems; Health; Health care; Information systems; Intelligent agents; Intensive care units, Critical Health Care; Intelligent environment; Intensive care; Online; Pervasive environments; Real-Time; Remote Connection, Medical computing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miranda2011253,
title = {Evolutionary intelligence in agent modeling and interoperability},
author = {M. Miranda and J. Machado and A. Abelha and J. Neves and J. Neves},
editor = {Corchado J. M. Preuveneers D. Novais P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052975755&doi=10.1007%2f978-3-642-19937-0_33&partnerID=40&md5=278dc339bbfa3d2f6334669b614558df},
doi = {10.1007/978-3-642-19937-0_33},
issn = {18675662},
year  = {2011},
date = {2011-01-01},
journal = {Advances in Intelligent and Soft Computing},
volume = {92},
pages = {253-257},
abstract = {A healthcare organization to be tuned with the users expectations, and to act according to its goals, must be accountable for the quality, cost, and overall care of the beneficiaries. In this paper we describe a model of clinical information designed to make health information systems properly interoperable and safely computable, based on an Evolutionary Intelligence approach that generates quantified scenarios from defective knowledge. The model is a response to a number of requirements, ranging from the semantic ones to the evaluation of software performance at runtime; it is among the biggest challenges in engineering nowadays. © 2011 Springer-Verlag Berlin Heidelberg.},
note = {cited By 2},
keywords = {Agent modeling; Clinical information; Health information systems; Healthcare organizations; Runtimes; Software performance, Health care; Intelligent agents; Intelligent systems; Interoperability; Semantics; Software agents, Multi agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Novais2011,
title = {Preface},
author = {P. Novais and J. Machado and C. Analide and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899020619&partnerID=40&md5=351affc07ca7d25f2f23e3589fe5f10f},
year  = {2011},
date = {2011-01-01},
journal = {ESM 2011 - 2011 European Simulation and Modelling Conference: Modelling and Simulation 2011},
pages = {IX},
publisher = {EUROSIS},
address = {Guimaraes},
note = {cited By 0; Conference of 25th European Simulation and Modelling Conference, ESM 2011 ; Conference Date: 24 October 2011 Through 26 October 2011; Conference Code:104378},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gongalves2011409,
title = {Object trajectory simulation - An evolutionary approach},
author = {P. Gongalves and L. Alves and T. Sá and C. Quintas and M. Miranda and A. Abelha and J. MacHado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899018956&partnerID=40&md5=a9b5426daa16b5fbb696ae8eaa0d561e},
year  = {2011},
date = {2011-01-01},
journal = {ESM 2011 - 2011 European Simulation and Modelling Conference: Modelling and Simulation 2011},
pages = {409-413},
publisher = {EUROSIS},
address = {Guimaraes},
abstract = {The ability to successfully predict the trajectory of an entity can have numerous interests. Using trajectory prediction we propose to enhance Radio-Frequency IDentification embedding intelligent behaviours that allow these systems to improve their accuracy in the detection and guidance of personal in RFID enabled infrastructures. This paper proposes a representative approach to a simulated area filled with sensors and travelled by an object. The object has an initial and end points and does random movement between them. The remaining unknown path must be provided by the sensors and the prevision module while error metrics must be calculated dynamically to help the prediction of the followed path. In this scenario, a trajectory is a path that an entity follows through space between iterations, and it can be represented as a set, of coordinates sorted over time. The level of accuracy needed for the prediction model and objectives of this environment required a grid like representation. The presented solution is a multiple dimension structure that covers the environ-ment variables and entities of a move within the environment. An application has been developed to simulate a censored place driven by a random trajectory object and to calculate as accurate as possible the path of the object. ©2011 EUROSIS-ETI.},
note = {cited By 1; Conference of 25th European Simulation and Modelling Conference, ESM 2011 ; Conference Date: 24 October 2011 Through 26 October 2011; Conference Code:104378},
keywords = {Data mining; Forecasting; Modal analysis; Radio frequency identification (RFID); Sensors, Error metrics; Evolutionary approach; Evolutionary intelligence; Multiple dimensions; Object trajectories; Prediction model; Space between; Trajectory prediction, Trajectories},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Peixoto2011399,
title = {AASYS - Appointment alert system: An open-source-based software to improve show rates in a health care unit},
author = {H. Peixoto and J. MacHado and A. Abelha and J. Neves and A. Correia and M. F. Santos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898994157&partnerID=40&md5=ff96b0afe3942de1f826644f5ffcb457},
year  = {2011},
date = {2011-01-01},
journal = {ESM 2011 - 2011 European Simulation and Modelling Conference: Modelling and Simulation 2011},
pages = {399-403},
publisher = {EUROSIS},
address = {Guimaraes},
abstract = {In this paper we show a practical approach to deal with government impositions correlated with on time appointments in major healthcare facilities in Portugal. Reduce waiting time and improve quality on patient communication are two of the main objectives in this project. Information Systems can improve patient communication with hospitals and build a trust and better patient-caregiver relationship. New possibilities upraise with new smart phones and it is up to us to use it according to national standards and laws. We intend to establish a platform named Appointment Alert System (AASYS) that aids healthcare facilities to decrease costs correlated with calls for appointments, which indeed one of the major sources of appointment department costs. Schedule administration is taken in consideration and an user interface web page was also built. At the end we prove that an alert system based on open source software leading to short message service arid email can help decrease costs and increase show rates. ©2011 EUROSIS-ETI.},
note = {cited By 2; Conference of 25th European Simulation and Modelling Conference, ESM 2011 ; Conference Date: 24 October 2011 Through 26 October 2011; Conference Code:104378},
keywords = {Alert systems; Healthcare facility; National standard; Open Source Software; Open sources; Portugal; Short message services; Waiting-time, Communication; Costs; Health care; Information systems; Modal analysis; Software engineering; User interfaces; Web services; Websites, Medical computing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Duarte2011414,
title = {Towards intelligent drug electronic prescription},
author = {J. Duarte and J. Neves and A. Cabral and M. Gomes and V. Marques and M. F. Santos and A. Abelha and J. MacHado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898976033&partnerID=40&md5=d0414e3997b901b8bbe1fab1017bceaa},
year  = {2011},
date = {2011-01-01},
journal = {ESM 2011 - 2011 European Simulation and Modelling Conference: Modelling and Simulation 2011},
pages = {414-418},
publisher = {EUROSIS},
address = {Guimaraes},
abstract = {The errors associated with prescription drugs arc common. The technology will help to reduce the error. The transition from the traditional method of prescription (written manually and on paper) for electronic prescribing of drugs has been done in developed countries. However, there is still some lack of efficicncy. Some of the inefficiencies in the method of electronic prescribing are related to the interface of these systems, based on forms. These systems arc still unwise and less useful as an aid in decision making on prescription. This study attempted to explore the automatic interpretation of text in electronic prescription systems and techniques of Case Based Reasoning for recommending drugs. ©2011 EUROSIS-ETI.},
note = {cited By 2; Conference of 25th European Simulation and Modelling Conference, ESM 2011 ; Conference Date: 24 October 2011 Through 26 October 2011; Conference Code:104378},
keywords = {Case based reasoning, Developed countries; Drug electronic prescription; Electronic health record; Electronic prescription system; Prescription drugs, Modal analysis; Ontology; Terminology},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela2011419,
title = {Enabling real-time intelligent decision support in intensive care},
author = {F. Portela and M. F. Santos and P. Gago and A. Silva and F. Rua and A. Abelha and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898952827&partnerID=40&md5=c145f0e0582ad54ee762733fb006d977},
isbn = {9789077381663},
year  = {2011},
date = {2011-01-01},
journal = {ESM 2011 - 2011 European Simulation and Modelling Conference: Modelling and Simulation 2011},
pages = {419-426},
publisher = {EUROSIS},
address = {Guimaraes},
abstract = {Medical devices in ICU allow for both continuous monitoring of patients and data collection. Nevertheless, the amount of data to be considered is such that it is difficult for doctors to extract all the useful knowledge. In order to help uncover some of that knowledge we have built an IDSS based in the agent's paradigm and using data mining techniques to build prediction models. With the intention of collecting as much data as possible the data acquisition process was automated. Furthermore, given the paramount importance of data quality for data mining a data quality agent responsible for detecting the errors in the data was devised. Indeed, data acquisition in the ICU is error prone as, for instance, sensors may be displaced as patients move. The aim of this paper is to present the overall KDD process implemented, presenting in detail the data transformations that were done and the benefits achieved. ©2011 EUROSIS-ETI.},
note = {cited By 15; Conference of 25th European Simulation and Modelling Conference, ESM 2011 ; Conference Date: 24 October 2011 Through 26 October 2011; Conference Code:104378},
keywords = {Acquisition process; Continuous monitoring; Data engineering; Data transformation; Intelligent decision support; KDD; Prediction model; Real-time, Agents; Biomedical equipment; Data acquisition; Decision support systems; Intelligent agents; Intensive care units; Modal analysis, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pontes2011387,
title = {A moral decision support system in medicine},
author = {G. Pontes and A. Duarte and D. Cuevas and M. Salazar and M. Miranda and A. Abelha and J. MacHado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898934013&partnerID=40&md5=fb0fc9618ae5bd3915813350f0a2f8df},
year  = {2011},
date = {2011-01-01},
journal = {ESM 2011 - 2011 European Simulation and Modelling Conference: Modelling and Simulation 2011},
pages = {387-391},
publisher = {EUROSIS},
address = {Guimaraes},
abstract = {Intensive Care Units are, in hospitals, special units where the use of ethics is common. Usually, there are few available beds and financial costs arc huge. In this paper, it is presented a model for the simulation of the allocation of resources, in an Intensive Care Unit. Since it is a problem that deals with human life, decisions must be supported by a sound reasoning process in order to cause the minimum damage. Therefore, it is important to introduce the concept of ethics and moral reasoning, in particular taking the maximum advantage of moral agents, which are entities capable of making intelligent decisions based on moral guidelines. These entities have also an intelligent behaviour, simulating a physician conduct when there is an overcrowding of patients in the Unit. The decision process is carried out based on the computation of some critical factors, including a death rate, the survival quality and financial costs. The death rate is achieved using SAPS3 algorithm. ©2011 EUROSIS-ETI.},
note = {cited By 0; Conference of 25th European Simulation and Modelling Conference, ESM 2011 ; Conference Date: 24 October 2011 Through 26 October 2011; Conference Code:104378},
keywords = {Artificial intelligence; Computer simulation; Decision support systems; Intensive care units; Modal analysis; Philosophical aspects; Population statistics, Critical factors; Decision process; Financial costs; Intelligent decisions; Moral agents; Moral reasoning; Reasoning; Reasoning process, Intelligent agents},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Dinis201086,
title = {Telemedicine as a tool for Europe-Africa cooperation: A practical experience},
author = {M. Dinis and F. Santiago and L. Silva and R. Ferreira and J. Machado and E. Castela},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885889845&doi=10.1007%2f978-3-642-12701-4_10&partnerID=40&md5=7045360e5a496cdc6c8a7f9500059d84},
doi = {10.1007/978-3-642-12701-4_10},
issn = {18678211},
year  = {2010},
date = {2010-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering},
volume = {38 LNICST},
pages = {86-94},
address = {Maputo},
abstract = {This paper presents the experience of an Europe-Africa telemedicine network, focused on the pediatric area, and involving hospitals located in Luanda (Angola), Benguela (Angola), Praia (Cape Verde) and Coimbra (Portugal). In the scope of this network, the cooperation between these hospitals goes beyond the teleconsultation sessions. Tele-training, clinical experience exchange, patient transfer agreements and health staff training to local development of new medical capabilities are some of the involved activities. It is therefore agreed that this kind of technical and knowledge network could also be expanded to other African countries with clear benefits to the local citizens, overcoming the digital-divide and improving the cooperation between developed and developing countries. © Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering 2010.},
note = {cited By 3; Conference of 1st International ICST Conference on E-Infrastructures and E-Services on Developing Countries, AFRICOMM 2009 ; Conference Date: 3 December 2009 Through 4 December 2009; Conference Code:86156},
keywords = {Africa; Benguela; Clinical experience; e-health; Knowledge networks; Local development; Patient transfer; Portugal; Practical experience; Staff training; Tele-echocardiography and telecommunications; Tele-training; Teleconsultation; Telehealth; Telemedicine networks, Developing countries; Echocardiography; Hospitals; Medical computing; Pediatrics; Personnel training, Telemedicine},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ribeiro201057,
title = {Integrating incomplete information into the relational data model},
author = {J. Ribeiro and J. Machado and A. Abelha and M. Fernandéz-Delgado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959817090&partnerID=40&md5=3febf9654879ef592dea6810494b22c1},
isbn = {9789881701299},
year  = {2010},
date = {2010-01-01},
journal = {WCE 2010 - World Congress on Engineering 2010},
volume = {1},
pages = {57-62},
address = {London},
abstract = {Knowledge and belief are generally incomplete, contradictory, or even error sensitive, being desirable to use formal tools to deal with the problems that arise from the use of partial, contradictory, ambiguous, imperfect, nebulous, or missing information. Historically, uncertain reasoning has been associated with probability theory. However, qualitative models and qualitative reasoning have been around in database theory and Artificial Intelligence research for some time, in particular due to the growing need to offer user support in decision making processes. In this paper, and under the umbrella of the Multi-valued Extended Logic Programming formalism to knowledge representation and reasoning we present an evaluative perspective of such an approach, in order to select the best theories (or logic programs) that model the universe of discourse to solve a problem, in terms of a process of quantification of the quality-of- information that stems out from those theories. Additionally, we present a novel approach to integrate incomplete information into the relational data model, making possible the use of the relational algebra operators and the potential inherent to the Structured Query Languages to present solutions to a particular problem and to measure their degree of self-reliance.},
note = {cited By 2; Conference of World Congress on Engineering 2010, WCE 2010 ; Conference Date: 30 June 2010 Through 2 July 2010; Conference Code:85243},
keywords = {Decision making; Decision support systems; Decision theory; Knowledge representation; Logic programming; Models; Problem solving; Query languages, Knowledge based systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inbook{Novais2010188,
title = {Agents, trust and contracts},
author = {P. Novais and F. Andrade and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959815393&doi=10.4018%2f978-1-61520-975-0.ch012&partnerID=40&md5=c2cd475feef0907ad1150a6eddd3c591},
doi = {10.4018/978-1-61520-975-0.ch012},
isbn = {9781615209750},
year  = {2010},
date = {2010-01-01},
journal = {Information Communication Technology Law, Protection and Access Rights: Global Approaches and Issues},
pages = {188-199},
publisher = {IGI Global},
abstract = {Inter-systemic contracting may be based upon autonomous intelligent behaviour. Autonomy is an important advantage of software agents. Yet, it brings along several issues concerning the legal consideration (e.g. legal personality/attribution) and the legal consequences of software agent's behaviour. The intervention of software agents in corporate bodies and the consideration of its roles must also be referred. All this intends interactions based on contracts and relations of trust, at an individual, at a community and at a systemic level. In this regard, it does make sense to speak of the relation between good faith and trust in inter-systemic contracting. And at the systemic level there is a need to focus on special protocols intended to enhance trust in electronic commerce. Smart contracts may be considered in this respect as a way of enhancing trust and of achieving enforcement in electronic contracting. t © 2010, IGI Global.},
note = {cited By 2},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Ribeiro2010,
title = {Handling incomplete information in an evolutionary environment},
author = {J. Ribeiro and J. Machado and A. Abelha and M. Fernandéz-Delgado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959483071&doi=10.1109%2fCEC.2010.5586079&partnerID=40&md5=e5abc9913bad1da5263f98012d26110e},
doi = {10.1109/CEC.2010.5586079},
isbn = {9781424469109},
year  = {2010},
date = {2010-01-01},
journal = {2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010},
address = {Barcelona},
abstract = {In this paper we address the problem of modeling creativity in Artificial Intelligence using a Genetic or Evolutionary based approach to computing, where the universe of discourse is represented as theories or programs in an extension to the Logic Programming language, which makes possible to handle incomplete or even contradictory information in an evolutionary environment. Indeed, we present a new insight for the construction of evolutive systems that combines the potential of the knowledge representation and reasoning mechanisms, present in the logic programming languages. Here, in an evolutionary setting, the candidate solutions to model the universe of discourse are seen as evolutionary logic programs or theories, being the test whether a solution is optimal based on a measure of the quality-of-information carried by those logical theories or programs. From a point of view of the process, the quality-of-information of the universe of discourse is assessed on the fly, being therefore possible to select the best logical theory or program that models it, in terms of the same time line. © 2010 IEEE.},
note = {cited By 2; Conference of 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 ; Conference Date: 18 July 2010 Through 23 July 2010; Conference Code:85187},
keywords = {Artificial intelligence, Candidate solution; Evolutionary settings; Incomplete information; Knowledge representation and reasoning; Logic programs; Logical theories; On the flies; Time line; Universe of discourse, Genetic programming; Knowledge representation; Logic programming},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Duarte2010201,
title = {Data quality evaluation of electronic health records in the hospital admission process},
author = {J. Duarte and M. Salazar and C. Quintas and M. Santos and J. Neves and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649311213&doi=10.1109%2fICIS.2010.97&partnerID=40&md5=39a72e5691605095d85a94bfad5f005f},
doi = {10.1109/ICIS.2010.97},
isbn = {9780769541471},
year  = {2010},
date = {2010-01-01},
journal = {Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010},
pages = {201-206},
address = {Yamagata},
abstract = {Data Quality Evaluation is a critical problem, specially in Healthcare, where people may take decisions based on confident, acceptable and secure information. In this paper we show how data quality can be evaluated from electronic health records, in particular in a hospital setting. We construct a dynamic virtual world of complex entities or agents, driven by one criterion alone, intelligence, for the provision of healthcare. This virtual world will witness the emergence and will be based on a versatile and powerful paradigm, where the candidate solutions (here understood as agents) are seen as evolutionary logic programs or theories, being the test if a solution is optimal based on a measure of the quality-of-information that stems from them. © 2010 IEEE.},
note = {cited By 23; Conference of 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 ; Conference Date: 18 August 2010 Through 20 August 2010; Conference Code:82351},
keywords = {Candidate solution; Complex entities; Critical problems; Data quality; Electronic health record; Extended logic programming; Hospital admissions; Hospital settings; Interoperations; Logic programs; Quality of information; Virtual worlds, Health; Hospitals; Information science; Logic programming; Records management; Social networking (online); Virtual reality, Quality control},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Portela2010195,
title = {Electronic health records in the emergency room},
author = {F. Portela and M. Vilas-Boas and M. F. Santos and A. Abelha and J. Machado and A. Cabral and I. Aragão},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649286974&doi=10.1109%2fICIS.2010.98&partnerID=40&md5=8b270837c1a63ade85711ba28ee2276d},
doi = {10.1109/ICIS.2010.98},
isbn = {9780769541471},
year  = {2010},
date = {2010-01-01},
journal = {Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010},
pages = {195-200},
address = {Yamagata},
abstract = {In an Emergency Room setting there is the pressure to make rapid, often critical decisions with incomplete information and this has a significant impact on care. However, it is difficult to provide high-quality, maximally safe, and efficient care in the Emergency Room. Information technology has the great potential for transforming the health care system, improving quality of care by providing the right information at the right time, to the right persons. This paper focuses on the expected impact of Information Systems and technology on the Emergency Room of Centro Hospitalar do Porto - Hospital de Santo António, Portugal. © 2010 IEEE.},
note = {cited By 9; Conference of 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 ; Conference Date: 18 August 2010 Through 20 August 2010; Conference Code:82351},
keywords = {Electronic health record; Emergency departments; Health-care system; High quality; Incomplete information; Portugal; Quality of care; Right information; Significant impacts, Emergency rooms; Health; Health care; Information science; Information systems; Records management, Medical computing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ribeiro2010183,
title = {The inference process with quality evaluation in healthcare environments},
author = {J. Ribeiro and A. Abelha and J. Machado and A. Marques and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649268622&doi=10.1109%2fICIS.2010.160&partnerID=40&md5=ea6e75d4229d65da60c4f998202906a8},
doi = {10.1109/ICIS.2010.160},
isbn = {9780769541471},
year  = {2010},
date = {2010-01-01},
journal = {Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010},
pages = {183-188},
address = {Yamagata},
abstract = {Intelligent Systems require the ability to reason with incomplete information, because in the real world complete information is hard to obtain, even in the most controlled situation. In recent years, many formalisms have been proposed tacking the matter of uncertain, incomplete in logic programs and databases. However, qualitative models and qualitative reasoning have been around in Artificial Intelligence research for some time, in particular due the growing need to offer support in decision-making processes. The evaluation of knowledge that stems out from logic programs becomes a point of research. The Quality-of-Information concept demonstrated their applicability in many dynamic environments and for decision making purposes. In this paper we present an illustrative example of the inference process in decisions in healthcare environments. Under the Extended Logic Programming paradigm to knowledge representation and reasoning, we present the evolutive perspective of the inference process to achieve logical programs (or theories) corresponding to the best theorems to solve a problem or take a decision. For the evaluation of the best theories we use a quantification of the quality-of-information that stems out from a logic program. © 2010 IEEE.},
note = {cited By 1; Conference of 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 ; Conference Date: 18 August 2010 Through 20 August 2010; Conference Code:82351},
keywords = {Computer software selection and evaluation; Decision making; Decision support systems; Decision theory; Information science; Intelligent systems; Knowledge representation; Logic programming, Quality control},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miranda201095,
title = {Modelling intelligent behaviours in multi-agent based HL7 services},
author = {M. Miranda and G. Pontes and P. Gonçalves and H. Peixoto and M. Santos and A. Abelha and J. MacHado},
editor = {Ishii N. Matsuo T. Lee R.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957310898&doi=10.1007%2f978-3-642-15405-8_9&partnerID=40&md5=f66c0e974ed2a0c26a29113a1c2d9de2},
doi = {10.1007/978-3-642-15405-8_9},
issn = {1860949X},
year  = {2010},
date = {2010-01-01},
journal = {Studies in Computational Intelligence},
volume = {317},
pages = {95-106},
abstract = {With the dissemination of Health Information Systems and the greater relevance of interoperability towards the quality of the information available to the clinical personnel, distinct architectures and methodologies have been devised in order to improve the existing platforms in the healthcare environment. However, most of them are based on HL7, an international standard for healthcare interoperability, which depending on the implementation as any technology has its advantages and limitations. This paper details the architecture and methodologies of a multi-agent based HL7 interoperation service. The mentioned system is incorporated in an integration platform, which is implemented in several healthcare institutions and uses Multi-Agent Systems to control and enable the flow of data and information within them. The log registry and extracted statistics of several years of interoperation in one institution are used to analyse the development of prediction models to imbue intelligent behaviour in the existing platform. The resulting models are studied and embedded into a validation HL7 server agent. © 2010 Springer-Verlag Berlin Heidelberg.},
note = {cited By 11},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{MacHado2010191,
title = {Morality in group decision support systems in medicine},
author = {J. MacHado and M. Miranda and G. Pontes and A. Abelha and J. Neves},
editor = {Badica C. Malgeri M. Essaaidi M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957265223&doi=10.1007%2f978-3-642-15211-5_20&partnerID=40&md5=fd6d2e4eabd4ee7083420f329463e60f},
doi = {10.1007/978-3-642-15211-5_20},
issn = {1860949X},
year  = {2010},
date = {2010-01-01},
journal = {Studies in Computational Intelligence},
volume = {315},
pages = {191-200},
abstract = {In this paper we intend to address the morality and behavior problems when using multi-agent systems as a methodology for problem solving when applied to Group Decision Support Systems in Medicine, where the universe of discourse is modeled in terms of a Multi-valued Extended Logic Program (MuvELP) language. It is also presented a formal method to assess moral decisions and moral behavior, accepted as a process of quantification of the Morally Preferable Relation (MPR), measured as the quantity of Quality-of-Information that stems from the logic programs that make the extension of MPR. On the other hand, it was also presented a process to qualify an extension of a logic program as a relational database, therefore taking advantage of the power of relational algebra for problem solving purposes, i.e to set the reason for which something is done or created or for which something exists. © 2010 Springer-Verlag Berlin Heidelberg.},
note = {cited By 3},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marques2010305,
title = {Archetype-based semantic interoperability in healthcare},
author = {A. Marques and A. Correia and L. Cerqueira and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956355835&partnerID=40&md5=76b2fe6a21ce9376a62c94b2eb0d1c78},
isbn = {9789896740221; 9789896740221},
year  = {2010},
date = {2010-01-01},
journal = {ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings},
volume = {2},
pages = {305-308},
address = {Valencia},
abstract = {Advances in new Methodologies for Problem Solving and Information Technology enable a fundamental redesign of health care processes based on the use and integration of data and/or knowledge at all levels, in a healthcare environment. Indeed, new communication technologies may support a transition from institution centric to patient-centric based applications, i.e., the health care system is faced with a series of challenges, namely those concerning quality-of-information and the cost-effectiveness of such processes. The distribution of cost-effective health care allowing the patient to take active part in the caring process, provision of evidence-based care on all levels in the system and effective use and reuse of information are key issues for the health care organization. The information and communication technology infrastructure should therefore reflect the view of the health care system as a seamless system where information can flow across organizational and professional borders. Therefore, in this work it will be address key principles that must be at the center of patient-centered technologies for disease management and prevention, namely those referred to above.},
note = {cited By 1; Conference of 2nd International Conference on Agents and Artificial Intelligence, ICAART 2010 ; Conference Date: 22 January 2010 Through 24 January 2010; Conference Code:81591},
keywords = {Active parts; Care process; Communication technologies; Disease management; Electronic health record; Health-care system; Healthcare environments; Healthcare organizations; Information and Communication Technologies; Key issues; Quality of information; Seamless system; Semantic interoperability, Artificial intelligence; Cost effectiveness; Health care; Information technology; Interoperability; Records management; Semantics, Health},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Machado2010436,
title = {Quality of service in healthcare units},
author = {J. Machado and A. Abelha and P. Novais and J. Neves and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937691217&doi=10.1504%2fIJCAET.2010.035396&partnerID=40&md5=028e1c0c4a0ee5c182bea98317481663},
doi = {10.1504/IJCAET.2010.035396},
issn = {17572657},
year  = {2010},
date = {2010-01-01},
journal = {International Journal of Computer Aided Engineering and Technology},
volume = {2},
number = {4},
pages = {436-449},
abstract = {Healthcare systems have to be understood in terms of a wide variety of heterogeneous, distributed and ubiquitous systems, speaking different languages, integrating medical equipment and being customised by different entities, which in turn were set by people living in different contexts and aiming at different goals. Therefore, architecture has been envisaged to support the medical applications in terms of an agency for integration, diffusion and archiving of medical information and the electronic medical record, a form of a web spider of intelligent information processing system, its major subsystems, their functional roles and the flow of information and control among them, with adjustable autonomy. With such web-based simulated systems, quality of service will be improved (e.g., the available knowledge may be used for educational and training purposes). © 2010 Inderscience Enterprises Ltd.},
note = {cited By 30},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Miranda201027,
title = {A step towards medical ethics modeling},
author = {M. Miranda and J. Machado and A. Abelha and G. Pontes and J. Neves},
editor = {Takeda H. Takeda H. Takeda H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921749103&doi=10.1007%2f978-3-642-15515-4_4&partnerID=40&md5=4aa78bb8c56a99fc5c8175f2f1920925},
doi = {10.1007/978-3-642-15515-4_4},
issn = {18684238},
year  = {2010},
date = {2010-01-01},
journal = {IFIP Advances in Information and Communication Technology},
volume = {335},
pages = {27-36},
publisher = {Springer New York LLC},
abstract = {Modeling of ethical reasoning has been a matter of discussion and research among distinct scientific fields, however no definite model has demonstrated undeniable global superiority over the others. However, the context of application of moral reasoning can require one methodology over the other. In areas such as medicine where quality of life and the life itself of a patient may be at stake, the ability to make the reasoning process understandable to staff and to change is of a paramount importance. In this paper we present some of the modeling lines of ethical reasoning applied to medicine, and defend that continuous logic programming presents potential for the development of trustworthy morally aware decision support systems. It is also presented a model of moral decision in two situations that emerge recurrently at the Intensive Care Units, a service where the moral complexity of regular decisions is a motivation for the analyze and development of moral decision support methodologies. © IFIP International Federation for Information Processing 2010.},
note = {cited By 0; Conference of 1st IMIA/IFIP Joint Symposium on E-Health, 2010 held as part of World Computer Congress, WCC 2010 ; Conference Date: 20 September 2010 Through 23 September 2010; Conference Code:112909},
keywords = {Artificial intelligence; Computer circuits; Decision support systems; Health; Intensive care units; Logic programming, Clinical ethics; Decision supports; Moral reasoning; Quality of life; Reasoning process; Scientific fields, Philosophical aspects},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Peixoto2010236,
title = {Semantic interoperability and health records},
author = {H. Peixoto and J. Machado and J. Neves and A. Abelha},
editor = {Takeda H. Takeda H. Takeda H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898958907&doi=10.1007%2f978-3-642-15515-4_30&partnerID=40&md5=a5010e69798b69d08a47288bc65a3a66},
doi = {10.1007/978-3-642-15515-4_30},
issn = {18684238},
year  = {2010},
date = {2010-01-01},
journal = {IFIP Advances in Information and Communication Technology},
volume = {335},
pages = {236-237},
publisher = {Springer New York LLC},
abstract = {Systems Interoperability and Electronic Health Records are responsible for an exponential number of visits in electronic repository, either in terms of medical professionals or related staff. This is paramount for a better and sustainable quality-of-care in clinical assistance and of great potential to medical research. Following these lines of though, we present an agency for the diffusion, integration and archiving of medical information, and show how semantic web can enforce the use of electronic documents in order to envisage free-paper hospitals. © IFIP International Federation for Information Processing 2010.},
note = {cited By 10; Conference of 1st IMIA/IFIP Joint Symposium on E-Health, 2010 held as part of World Computer Congress, WCC 2010 ; Conference Date: 20 September 2010 Through 23 September 2010; Conference Code:112909},
keywords = {Clinical research; Health care; Information systems; Medical computing; Records management; Semantics, Electronic document; Electronic health record; Electronic repositories; Exponential numbers; Medical information; Medical professionals; Semantic interoperability; Systems interoperability, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Alves2010266,
title = {Interoperability performance in a healthcare environment},
author = {F. Alves and A. Abelha and J. Machado and J. Neves and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898661870&partnerID=40&md5=8d16768c682caf8c3fc2c87ae7ace186},
year  = {2010},
date = {2010-01-01},
journal = {ESM 2010 - 2010 European Simulation and Modelling Conference},
pages = {266-270},
publisher = {EUROSIS},
address = {Hasselt},
abstract = {HL7(Health Level 7) provides interoperability standards that improve patient treatment, optimize the work flow, reduce the ambiguity and improve the knowledge transfer between healthcare facilities, governmental agencies and the provider community, i.e., all these processes must be handled with scientific accuracy and technical ability, with compromising transparency, responsibility and practicability. Indeed, any healthcare application need to communicate with its peers, once their added value is based on integration. In order to accomplish this goal we will consider performance studies of HL7 messages on a client-server architecture built on Mirth Connect and tested with client and server running on localhost, client and server on separate machines but in the same network and client and server on different machines and different networks.},
note = {cited By 0; Conference of 24th Annual European Simulation and Modelling Conference, ESM 2010 ; Conference Date: 25 October 2010 Through 27 October 2010; Conference Code:104375},
keywords = {Client server computer systems; Knowledge management; Modal analysis; Patient treatment, Client-server architectures; Governmental agency; Health care application; Health level 7; Healthcare environments; Healthcare facility; Mirth Connect; Performance, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miranda2010261,
title = {Interoperabity in healthcare},
author = {M. Miranda and J. Duarte and A. Abelha and J. Machado and J. Neves and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898614025&partnerID=40&md5=d2ac4cd52e1664aa63e3d66b04a3b331},
year  = {2010},
date = {2010-01-01},
journal = {ESM 2010 - 2010 European Simulation and Modelling Conference},
pages = {261-265},
publisher = {EUROSIS},
address = {Hasselt},
abstract = {Hospital Information Systems need to communicate in order to share information and to make it available at anyplace at whatever time. Indeed, these systems have to go along with the fundamentals of ubiquity and quality-of-care, being embedded in some forms of intelligent mechanisms in order to be useful for medical, clinical and administrative staff. In fact, to fulfill this goal, the information available must be judge in terms of its quality, acquired via a process of quantification of the extensions of the predicates that make their realm, i.e., speaking for a high degree of confidence on it on the part of the users. Admittedly, centralized systems are not a solution, they speak for themselves. The answer, once one must be able to exchange and make use of information, is interoperability.},
note = {cited By 7; Conference of 24th Annual European Simulation and Modelling Conference, ESM 2010 ; Conference Date: 25 October 2010 Through 27 October 2010; Conference Code:104375},
keywords = {Administrative staff; Centralized systems; Degree of confidence; Healthcare Interoperability; Hospital information systems; Intelligent mechanisms, Embedded systems; Interoperability; Modal analysis, Health care},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Machado201067,
title = {AIDATrace - Interoperation platform for active monitoring in healthcare environments},
author = {J. Machado and M. Miranda and P. Gonçalves and A. Abelha and J. Neves and A. Marques},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898481898&partnerID=40&md5=2f89cf34582d929f0e5b7caefe647d31},
year  = {2010},
date = {2010-01-01},
journal = {8th International Industrial Simulation Conference 2010, ISC 2010},
pages = {67-71},
publisher = {EUROSIS},
address = {Budapest},
abstract = {The introduction of monitoring systems may have a great potential to introduce Ambient Intelligence based monitoring techniques in healthcare environments. On the other hand, current research being sponsored by the European Union presents interoperability issues as a considerable obstacle to implement and fully explore the capabilities of such a technology. Therefore, and in order to contribute to overcome this drawback, in this paper we address the different methodologies put into operation in the healthcare sector, supported by a putative architecture which has been used in different healthcare institutions to suport RFID monitoring systems.},
note = {cited By 6; Conference of 2010 8th International Industrial Simulation Conference, ISC 2010 ; Conference Date: 7 June 2010 Through 9 June 2010; Conference Code:104331},
keywords = {Active monitoring; Ambient intelligence; Healthcare environments; Healthcare institutions; Healthcare sectors; HL7; Monitoring system; Monitoring techniques, Health care, Interoperability; Monitoring; Radio frequency identification (RFID)},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Andrade2010352,
title = {Software agents and virtual organisations: Consent and trust},
author = {F. Andrade and P. Novais and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77951442612&doi=10.1504%2fIJSOM.2010.031958&partnerID=40&md5=fc965459bc4754745feaef342c15a292},
doi = {10.1504/IJSOM.2010.031958},
issn = {17442370},
year  = {2010},
date = {2010-01-01},
journal = {International Journal of Services and Operations Management},
volume = {6},
number = {3},
pages = {352-361},
publisher = {Inderscience Publishers},
abstract = {The use of software agents in electronic commerce scenarios must be connected to the existence of corporate bodies and virtual organisations. Indeed, software agents tend to play a determinant role in corporate bodies and Virtual Enterprises (VEs). However, it must be considered that software agents' behaviours are not entirely foreseeable, since they are able to behave with good or bad faith. Thus, it is of utmost relevance to consider the issue of trust in software agents, both at the individual and systemic level. At the systemic level, it will be interesting to consider smart contracts, which may well enhance trust in electronic contracting. Copyright © 2010 Inderscience Enterprises Ltd.},
note = {cited By 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Costa2009138,
title = {VirtualECare: Intelligent assisted living},
author = {R. Costa and P. Novais and L. Lima and D. Carneiro and D. Samico and J. Oliveira and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885886293&doi=10.1007%2f978-3-642-00413-1_17&partnerID=40&md5=c95a485703b42027cd0737104d7255e0},
doi = {10.1007/978-3-642-00413-1_17},
issn = {18678211},
year  = {2009},
date = {2009-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering},
volume = {1 LNICST},
pages = {138-144},
address = {London},
abstract = {Innovative healthcare projects are arising in today's society, normally presenting as major advantage the reduction of care provider's costs. Being these advantage a legitimate one, we are trying to take it a step forward through the use of proactiveness, decision making techniques, idea generation, argumentation and quality, not only of the in transit information, but also of the provided service as well. With these objectives in mind, the VirtualECare project was born. In this paper we are going to briefly present the project and make a position of the actual developments in this first year of work. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009.},
note = {cited By 18; Conference of 1st International Conference on Electronic Healthcare, eHealth 2008 ; Conference Date: 8 September 2008 Through 9 September 2008; Conference Code:85965},
keywords = {Active ageing; Ambient intelligence; Assisted living; Ehealth; First year; Idea generation; Proactiveness, Decision making, Health care},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miranda2009114,
title = {A group decision support system for staging of cancer},
author = {M. Miranda and A. Abelha and M. Santos and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870914962&doi=10.1007%2f978-3-642-00413-1_14&partnerID=40&md5=49bac329715400e5bec9a926be3ae06b},
doi = {10.1007/978-3-642-00413-1_14},
issn = {18678211},
year  = {2009},
date = {2009-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering},
volume = {1 LNICST},
pages = {114-121},
address = {London},
abstract = {The TNM classification system was developed as a tool for physicians to stage different types of cancer based on standard criteria, according to a common language of cancer staging. Staging reports are usually performed by oncologists but sometimes are also done by physicians not specialized in this area. In this paper, it is presented a multi-agent system to support group decision that helps meeting participants to reach and to justify a solution. With the increasing use of web applications to perform the Electronic Medical Record on healthcare facilities, this system has the potential to be easily integrated in order to support the medical and clinical e-learning and to improve patient assistance. In fact, the usual need for documentation and specific information by the medical staff can be easily provided by these systems, making a new steep towards a paper free healthcare system. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009.},
note = {cited By 16; Conference of 1st International Conference on Electronic Healthcare, eHealth 2008 ; Conference Date: 8 September 2008 Through 9 September 2008; Conference Code:85965},
keywords = {Cancer staging; Classification system; Common languages; Ehealth; Electronic medical record; Group decision; Health-care system; Healthcare facility; Specific information; Staging of caner; WEB application, Decision making; Diseases; E-learning; Health care; Intelligent agents; Medical computing; Multi agent systems; User interfaces, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Duarte2009115,
title = {Agent-based group decision support in medicine},
author = {J. Duarte and M. Miranda and A. Abelha and M. Santos and J. Machado and J. Neves and C. Alberto and M. Salazar and C. Quintas and A. Ferreira and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957293677&partnerID=40&md5=6d7d94cef7bf789231dbb1e484549eb0},
isbn = {9781601321091},
year  = {2009},
date = {2009-01-01},
journal = {Proceedings of the 2009 International Conference on Artificial Intelligence, ICAI 2009},
volume = {1},
pages = {115-121},
address = {Las Vegas, NV},
abstract = {Cancer staging is the process by which physicians evaluate the spread of cancer. This is important, once in a good cancer staging system, the stage of disease helps to determine prognosis and assists in selecting therapies. A combination of physical examination, blood tests, and medical imaging is used to determine the clinical stage; if tissue is obtained via biopsy or surgery, examination of the tissue under a microscope can provide pathologic staging. On the other hand, good patient education may help to reduce health service costs and improve the quality of life of people with chronic or terminal conditions. In this paper it is endorsed a theoretical based model to support the provision of computer based information on cancer patients, and the computational techniques used to implement it. One's goal is to develop an interactive agent based computational system which may provide physicians with the right information, on time, that is adapted to the situation and process-based aspects of the patients's illness and treatment.},
note = {cited By 2; Conference of 2009 International Conference on Artificial Intelligence, ICAI 2009 ; Conference Date: 13 July 2009 Through 16 July 2009; Conference Code:92509},
keywords = {Agent based; Blood test; Cancer patients; Cancer staging; Clinical intelligence; Computational system; Computational technique; Group decision supports; Health services; Interactive agents; Medical informatics; Patient education; Quality of life; Right information, Artificial intelligence; Decision support systems; Diagnosis; Disease control; Intelligent agents; Medical imaging; Patient treatment; Tissue, Diseases},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Costa2009209,
title = {A memory assistant for the elderly},
author = {A. Costa and P. Novais and R. Costa and J. MacHado and J. Neves},
editor = {Papadopoulos G.A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957286581&doi=10.1007%2f978-3-642-03214-1_21&partnerID=40&md5=47330198b4f6f2a803a42981fae0656e},
doi = {10.1007/978-3-642-03214-1_21},
issn = {1860949X},
year  = {2009},
date = {2009-01-01},
journal = {Studies in Computational Intelligence},
volume = {237},
pages = {209-214},
abstract = {In the present-day the ageing population is not receiving the proper attention and care they need, because there aren't enough healthcare providers to everyone. As the elderly relatives have less time to take care of them the healthcare centers are without any doubt insufficient for all, for these reasons there is an extra pressure or demand on the healthcare sector. Focusing on the elderly care, as the human capacity of memorizing events decreases over time, it is intended to promote an active ageing lifestyle for the elderly, where memory assistance tools are vital component. In this paper, it is presented an scheduler which takes charge of the day-to-day tasks and the user agenda. © 2009 Springer-Verlag Berlin Heidelberg.},
note = {cited By 3},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Santos2009810,
title = {Information architecture for intelligent decision support in intensive medicine},
author = {M. F. Santos and F. Portela and M. Vilas-Boas and J. Machado and A. Abelha and J. Neves and A. Silva and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-69549098007&partnerID=40&md5=eadb2fee9457fd205183fd9b3262ce5b},
issn = {11092750},
year  = {2009},
date = {2009-01-01},
journal = {WSEAS Transactions on Computers},
volume = {8},
number = {5},
pages = {810-819},
abstract = {Daily, a great amount of data that is gathered in intensive care units, which makes intensive medicine a very attractive field for applying knowledge discovery in databases. Previously unknown knowledge can be extracted from that data in order to create prediction and decision models. The challenge is to perform those tasks in real-time, in order to assist the doctors in the decision making process. Furthermore, the models should be continuously assessed and optimized, if necessary, to maintain a certain accuracy. In this paper we propose an information architecture to support an adjustment to the INTCare system, an intelligent decision support system for intensive medicine. We focus on the automatization of data acquisition avoiding human intervention, describing its steps and some requirements.},
note = {cited By 18},
keywords = {Architectural design; Artificial intelligence; Database systems; Decision support systems; Decision theory; Information retrieval; Information science; Intensive care units; Knowledge management; Mergers and acquisitions; Real time systems, Decision making, Information models; INTCare; Intelligent decision support systems; Intensive care; Knowledge discovery in databases; Real-time data acquisition},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Costa200986,
title = {Ambient assisted living},
author = {R. Costa and D. Carneiro and P. Novais and L. Lima and J. Machado and A. Marques and J. Neves},
editor = {Bravo J. Corchado J.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-58149140077&doi=10.1007%2f978-3-540-85867-6_10&partnerID=40&md5=19f0ad4d87ed2b4284216dee1f8a4a9f},
doi = {10.1007/978-3-540-85867-6_10},
issn = {16153871},
year  = {2009},
date = {2009-01-01},
journal = {Advances in Soft Computing},
volume = {51},
pages = {86-94},
abstract = {The quality of care practice is difficult to judge. Indeed, support and care provision is very personal, i.e., assessments are individual and lead to specific care packages, involving social services, health workers, care agencies. We expect privacy in our own affairs and confidentially from those to whom we disclose them. Therefore, we are in an urgent need for new, technological and formal approaches to problem solving, as the increase of population with special care requirements. Following this line of thought, it is one's goal to present the VirtualECare framework, an intelligent multi-agent system able to monitor, interact and serve its customers, which are in need of care services, based in open standards, expecting not only to fulfil the objectives referred to above, but also to overcome the problems induced by the use of new technologies and formalisms. © 2009 Springer-Verlag Berlin Heidelberg.},
note = {cited By 41},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Marreiros2009182,
title = {Argument-based decision making in ambient intelligence environments},
author = {G. Marreiros and C. Ramos and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933525207&doi=10.1504%2fIJRIS.2009.028017&partnerID=40&md5=03938a18e7087579eda03e023f79875e},
doi = {10.1504/IJRIS.2009.028017},
issn = {17550556},
year  = {2009},
date = {2009-01-01},
journal = {International Journal of Reasoning-based Intelligent Systems},
volume = {1},
number = {3-4},
pages = {182-190},
abstract = {Decision making is a cognitive process of the foremost importance to set a line of action under change; it is said to be a psychological construct, being therefore latent and not directly observable. Indeed, issues of construct validity are paramount, where the information gathered would provide insight regarding the construct of emotional intelligence and how one would attempt to clarify its meaning and measure it. In this paper, this problem will be addressed in terms of an argumentation-based system, where intelligent agents simulate the behaviour of individuals as group members that take part in a decision making process. © 2009 Inderscience Enterprises Ltd.},
note = {cited By 3},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Miranda2009205,
title = {Interoperability and healthcare},
author = {M. Miranda and J. Duarte and A. Abelha and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898619959&partnerID=40&md5=f7989a0ae6b9affd1048f8f792cbbb61},
year  = {2009},
date = {2009-01-01},
journal = {ESM 2009 - 2009 European Simulation and Modelling Conference: Modelling and Simulation 2009},
pages = {205-212},
publisher = {EUROSIS},
address = {Leicester},
abstract = {In our research group work is being conducted on how to develop self organised Engineered Information Systems (EIS), which could enable researchers to create programs with significantly improved functionalities, leading to a more efficient and faster computation. These EIS are based on an original DNA origami which may be designed to serve as a scaffold for electronic workers (or software agents), going beyond existing technology either in terms of Socialization, Interoperability or the process of quantification of the Quality-of-Information (Qol) being exploited.},
note = {cited By 8; Conference of 23rd European Simulation and Modelling Conference, ESM 2009 ; Conference Date: 26 October 2009 Through 28 October 2009; Conference Code:104370},
keywords = {Dna origamis; Quality-of-information; Research groups; Self-organised, Health care; Information systems; Integration; Medical computing; Modal analysis; Multi agent systems; Scaffolds, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Belo2009203,
title = {Quality of service in transplantation via the electronic medical record},
author = {D. Belo and M. Miranda and A. Abelha and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898461542&partnerID=40&md5=9db98f63e2a945c38373d2d2fe757d27},
year  = {2009},
date = {2009-01-01},
journal = {7th International Industrial Simulation Conference 2009, ISC 2009},
pages = {203-208},
publisher = {EUROSIS},
address = {Loughborough},
abstract = {Medical Informatics may sets itself as a new area of research open to share its practices, but with a predisposition to consider different but complementary computational paradigms and new methodologies for problem solving. Indeed, healthcare providers, namely the institutions on the public sector, with its physicians, nurses, administrative staff and patients present the right universe to consider and study. It is in this context that the use of Electronic Health Records changed the workflow of facilities which use heterogeneous, scheduled and unstructured methods of recording, in order to solve patient problems and to improve medical and clinical research and education. In this paper the Electronic Health Record of the Centro Hospitalar do Porto (one of the major Portuguese healthcare facilities in the public sector) is presented. The focus is made on the integration and recording of different processes, being studied the case of the hepatic transplantation surgery department.},
note = {cited By 0; Conference of 2009 7th International Industrial Simulation Conference, ISC 2009 ; Conference Date: 1 June 2009 Through 3 June 2009; Conference Code:104329},
keywords = {Administrative staff; Computational paradigm; Electronic health record; Electronic medical record; Health care providers; Hepatic transplantation; Medical informatics; Unstructured methods, Health care; Medical computing; Medical problems; Records management, Quality of service},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Machado2009112,
title = {Modeling Medical Ethics through Intelligent Agents},
author = {J. Machado and M. Miranda and A. Abelha and J. Neves and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84882958807&doi=10.1007%2f978-3-642-04280-5_10&partnerID=40&md5=be83e457231c9884632a49dc3f413ccd},
doi = {10.1007/978-3-642-04280-5_10},
issn = {18684238},
year  = {2009},
date = {2009-01-01},
journal = {IFIP Advances in Information and Communication Technology},
volume = {305},
pages = {112-122},
publisher = {Springer New York LLC},
address = {Nancy},
abstract = {The amount of research using health information has increased dramatically over the last past years. Indeed, a significative number of healthcare institutions have extensive Electronic Health Records (EHR), collected over several years for clinical and teaching purposes, but are uncertain as to the proper circumstances in which to use them to improve the delivery of care to the ones in need. Research Ethics Boards in Portugal and elsewhere in the world are grappling with these issues, but lack clear guidance regarding their role in the creation of and access to EHRs. However, we feel we have an effective way to handle Medical Ethics if we look to the problem under a structured and more rational way. Indeed, we felt that physicians were not aware of the relevance of the subject in their pre-clinical years, but their interest increase when they were exposed to patients. On the other hand, once EHRs are stored in machines, we also felt that we had to find a way to ensure that the behavior of machines toward human users, and perhaps other machines as well, is ethically acceptable. Therefore, in this article we discuss the importance of machine ethics and the need for machines that represent ethical principles explicitly. It is also shown how a machine may abstract an ethical principle from a logical representation of ethical judgments and use that principle to guide its own behavior. © IFIP International Federation for Information Processing 2009.},
note = {cited By 6; Conference of 9th IFIP WG 6.1 Conference on e-Business, e-Services and e-Society, I3E 2009 ; Conference Date: 23 September 2009 Through 25 September 2009; Conference Code:98939},
keywords = {Abstracting; Electronic commerce; Felt; Intelligent agents, Electronic health record; Ethical principles; Health informations; Healthcare institutions; Logical representations; medical ethics; Morality; Research ethics, Philosophical aspects},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miranda2009313,
title = {An agent-based architecture for cancer staging},
author = {M. Miranda and A. Abelha and M. Santos and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-74549205928&doi=10.5220%2f0001863503130316&partnerID=40&md5=0a0e694e2a6f23cab3c5503c39d39170},
doi = {10.5220/0001863503130316},
isbn = {9789898111845},
year  = {2009},
date = {2009-01-01},
journal = {ICEIS 2009 - 11th International Conference on Enterprise Information Systems, Proceedings},
volume = {AIDSS},
pages = {313-316},
publisher = {INSTICC Press},
address = {Milan},
abstract = {Cancer staging is the process by which physicians evaluate the spread of cancer. This is important, once in a good cancer staging system, the stage of disease helps to determine prognosis and assists in selecting ther apies. A combination of physical examination, blood tests, and medical imaging is used to determine the clinical stage; if tissue is obtained via biopsy or surgery, examination of the tissue under a microscope can provide pathologic staging. On the other hand, good patient education may help to reduce health service costs and improve the quality of life of people with chronic or terminal conditions. In this paper it is endorsed a theoretical based model to support the provision of computer based information on cancer patients, and the computational techniques used to implement it. One's goal is to develop an interactive agent based computa-tional system which may provide physicians with the right information, on time, that is adapted to the situation and process-based aspects of the patients's illness and treatment.},
note = {cited By 0; Conference of ICEIS 2009 - 11th International Conference on Enterprise Information Systems ; Conference Date: 6 May 2009 Through 10 May 2009; Conference Code:79065},
keywords = {Agent based architectures; Cancer patients; Cancer staging; Computational technique; Health services; Interactive agents; Patient education; Quality of life, Artificial intelligence; Decision support systems; Diagnosis; Information systems; Information use; Medical computing; Medical imaging; Patient treatment; Tissue, Diseases},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Analide200835,
title = {An agent based approach to the selection dilemma in CBR},
author = {C. Analide and A. Abelha and J. Machado and J. Neves},
editor = {Mangioni G. M. Burdescu D.D.B. Badica C.B.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-51849141863&doi=10.1007%2f978-3-540-85257-5_4&partnerID=40&md5=398ba95e64599f60c5c14c224a99ce0b},
doi = {10.1007/978-3-540-85257-5_4},
issn = {1860949X},
year  = {2008},
date = {2008-01-01},
journal = {Studies in Computational Intelligence},
volume = {162},
pages = {35-44},
abstract = {It is our understanding that a selection algorithm in Case Based Reasoning (CBR) must not only apply the principles of evolution found in nature, to the predicament of finding an optimal solution, but to be assisted by a methodology for problem solving based on the concept of agent. On the other hand, a drawback of any evolutionary algorithm is that a solution is better only in comparison to other(s), presently known solutions; such an algorithm actually has no concept of an optimal solution, or any way to test whether a solution is optimal. In this paper it is addressed the problem of The Selection Dilemma in CBR, where the candidate solutions are seen as evolutionary logic programs or theories, here understood as making the core of computational entities or agents, being the test whether a solution is optimal based on a measure of the quality-of-information that stems out of them. © 2008 Springer-Verlag Berlin Heidelberg.},
note = {cited By 6},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Andrade2008389,
title = {Software agents in virtual organizations: Good faith and trust},
author = {F. Andrade and P. Novais and J. Machado and J. Neves},
editor = {Picard W. P. Camarinha-Matos L.M.C.-M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-51149085006&doi=10.1007%2f978-0-387-84837-2_40&partnerID=40&md5=c3ecde63df22a56fcea18494e9c0ad12},
doi = {10.1007/978-0-387-84837-2_40},
issn = {15715736},
year  = {2008},
date = {2008-01-01},
journal = {IFIP International Federation for Information Processing},
volume = {283},
pages = {389-396},
abstract = {Virtual organizations tend to play an ever more part in electronic commerce, as well as software agents, here understood as the building blocks of the methodology for problem solving that is being subscribed. Indeed, one of the issues that have to be addressed is the capability of such entities to rationally and autonomously "think" and decide. The behavior of these agents may go more and more unpredictable; they will choose their own strategies and define their own planning where are faced to a problem, being possible that they may act with good faith or with bad faith. This leads us to the absolute need of considering the major issue of trust in software agent's environments. © 2008 International Federation for Information Processing.},
note = {cited By 0},
keywords = {Bad faith; Building blockes; Good faith; Virtual organization, Software agents, Virtual corporation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Novais2008353,
title = {Group support in collaborative networks organizations for ambient assisted living},
author = {P. Novais and R. Costa and D. Carneiro and J. Machado and L. Lima and J. Neves},
editor = {Yasunobu C. Y. Uda R.U. Oya M.O.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-50249131432&doi=10.1007%2f978-0-387-85691-9_30&partnerID=40&md5=e00e3112ea1c07a159456309ac0d9aaf},
doi = {10.1007/978-0-387-85691-9_30},
issn = {15715736},
year  = {2008},
date = {2008-01-01},
journal = {IFIP International Federation for Information Processing},
volume = {286},
pages = {353-362},
abstract = {Collaborative Work plays an important role in today's organizations and normally in areas where decisions must be made. However, any decision that involves a collective or group of decision makers is, by itself, complex. In this work we present the VirtualECare project, built in terms of an intelligent multi-agent system able to monitor, interact and serve its customers, which are, normally, in need of care services, and assisted with tools based on open standards, like OSGi an R-OSGi. © 2008 International Federation for Information Processing.},
note = {cited By 7},
keywords = {Ambient assisted living; Collaborative network; Collaborative Work; Decision makers; Group supports; Intelligent multi agent systems; Open Standards, Complex networks; Complex networks, Electronic commerce; Multi agent systems; World Wide Web; Decision making; Intelligent agents; Multi agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Carneiro2008175,
title = {Simulating and monitoring ambient assisted living},
author = {D. Carneiro and R. Costa and P. Novais and J. Neves and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898929059&partnerID=40&md5=96556202044eb174de5b77651e55cff9},
year  = {2008},
date = {2008-01-01},
journal = {ESM 2008 - 2008 European Simulation and Modelling Conference: Modelling and Simulation 2008},
pages = {175-182},
publisher = {EUROSIS},
address = {Le Havre},
abstract = {Researchers are giving special attention to innovative healthcare projects in order to reduce medical service costs and to deal with the population ageing. The virtual e-care was born taking those goals in mind. An intelligent and proactive system has been prototyped, supporting group decision making techniques, idea generation, argumentation and the quantification of the quality of information. In this paper it is simulated a virtual assisted living environment, based in a solid agent-based architecture. Special attention is given to the monitoring system. © 2008 EUROSIS-ETI.},
note = {cited By 23; Conference of 22nd Annual European Simulation and Modelling Conference, ESM 2008 ; Conference Date: 27 October 2008 Through 29 October 2008; Conference Code:104367},
keywords = {Agent-based architecture; Ambient assisted living; Ambient intelligence; Ehealth; Group Decision Making; Healthcare projects; Quality of information; Remote monitoring, Decision making, Modal analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Machado2008291,
title = {Quality of service in healthcare units},
author = {J. Machado and A. Abelha and P. Novais and J. Neves and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957277590&partnerID=40&md5=e430ede4a811409da273642f44627a87},
year  = {2008},
date = {2008-01-01},
journal = {ESM 2008 - 2008 European Simulation and Modelling Conference: Modelling and Simulation 2008},
pages = {291-298},
publisher = {EUROSIS},
address = {Le Havre},
abstract = {Heathcare systems have to be understood in terms of a wide variety of heterogeneous, distributed and ubiquitous systems, speaking different languages, integrating medical equipment and being customized by different companies, which in turn were developed by people aiming at different goals. An architecture has been envisaged to support the medical applications in terms of an agency for integration, diffusion and archiving of medical information and the Electronic Medical Record, a form of a web spider of an intelligent information processing system, its major subsystems, their functional roles, and the flow of information and control among them, with adjustable autonomy. With such web based simulated systems, quality of service will be improved (e.g., the available knowledge may be used for educational and training purposes). © 2008 EUROSIS-ETI.},
note = {cited By 55; Conference of 22nd Annual European Simulation and Modelling Conference, ESM 2008 ; Conference Date: 27 October 2008 Through 29 October 2008; Conference Code:104367},
keywords = {Adjustable autonomy; Electronic medical record; Intelligent information processing systems; Medical information; Simulated system; Training purpose; Ubiquitous systems; Web spiders, Medical applications; Medical computing; Modal analysis, Quality of service},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Rigor2008264,
title = {A web-based system to reduce the nosocomial infection impact in healtcare units},
author = {H. Rigor and J. Machado and A. Abelha and J. Neves and C. Alberto},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-58049169298&partnerID=40&md5=de3d7876b35defa00c5b4e3fc1e485f7},
isbn = {9789898111265},
year  = {2008},
date = {2008-01-01},
journal = {WEBIST 2008 - 4th International Conference on Web Information Systems and Technologies, Proceedings},
volume = {1},
pages = {264-268},
address = {Funchal, Madeira},
abstract = {The nosocomial infection has a critical impact on the mortality and morbidity of the patients in healthcare units, especially in intensive care units, and has been studied in order to be mitigated. The registration of information about this phenomenon in databases is, more and more, a reality, turning viable the representation of this information through mathematical formalisms that, conjugated with the application of Artificial Intelligence techniques, will allow the discovery of knowledge related with the critical factors, processes and infectious agents. The ultimate goal has been to construct a web-based computational tool to automate the registration process to support the clinical body work, monitoring the performance and identifying the procedures that can be implemented in order to reduce the impact of the infections.},
note = {cited By 10; Conference of WEBIST 2008 - 4th International Conference on Web Information Systems and Technologies ; Conference Date: 4 May 2008 Through 7 May 2008; Conference Code:74937},
keywords = {Artificial intelligence; Database systems; E-learning; Information systems; Intensive care units; Internet; Knowledge representation; Multimedia systems; Process engineering, Computational tools; Critical factors; Databases; Infectious agents; Mathematical formalisms; Registration processes; System integration; Web engineering; Web-based systems, World Wide Web},
pubstate = {published},
tppubtype = {inproceedings}
}
@book{Neves2007,
title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics: Preface},
author = {J. Neves and M. Santos and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38349054358&partnerID=40&md5=ddc3d2a9f5b088f535b4a4a253ad084e},
issn = {03029743},
year  = {2007},
date = {2007-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {4874 LNAI},
pages = {V-VI},
address = {Guimaraes},
note = {cited By 0; Conference of Ot13th Portuguese Conference on Artificial Intelligence, EPIA 2007 Workshops ; Conference Date: 3 December 2007 Through 7 December 2007; Conference Code:71232},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
@article{Andrade2007357,
title = {Contracting agents: Legal personality and representation},
author = {F. Andrade and P. Novais and J. MacHado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-35948946448&doi=10.1007%2fs10506-007-9046-0&partnerID=40&md5=d3d1ba1ed1775c04412dd68b6a3401a7},
doi = {10.1007/s10506-007-9046-0},
issn = {09248463},
year  = {2007},
date = {2007-01-01},
journal = {Artificial Intelligence and Law},
volume = {15},
number = {4},
pages = {357-373},
abstract = {The combined use of computers and telecommunications and the latest evolution in the field of Artificial Intelligence brought along new ways of contracting and of expressing will and declarations. The question is, how far we can go in considering computer intelligence and autonomy, how can we legally deal with a new form of electronic behaviour capable of autonomous action? In the field of contracting, through Intelligent Electronic Agents, there is an imperious need of analysing the question of expression of consent, and two main possibilities have been proposed: considering electronic devices as mere machines or tools, or considering electronic devices as legal persons. Another possibility that has been frequently mentioned consists in the application of the rules of agency to electronic transactions. Meanwhile, the question remains: would it possible, under a Civil Law framework, to apply the notions of "legal personhood" and "representation" to electronic agents? It is obvious that existing legal norms are not fit for such an endeavouring challenge. Yet, the virtual world exists and it requires a new but realistic legal approach on software agents, in order to enhance the use of electronic commerce in a global world. © 2007 Springer Science+Business Media B.V.},
note = {cited By 38},
keywords = {Artificial intelligence; Electronic commerce; Laws and legislation; Outsourcing; Telecommunication services, Autonomous agents, Civil Law framework; Electronic agents; Legal personhood},
pubstate = {published},
tppubtype = {article}
}
@article{Andrade2007217,
title = {Intelligent contracting: Software agents, corporate bodies and Virtual Organizations},
author = {F. Andrade and P. Novais and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548852560&doi=10.1007%2f978-0-387-73798-0_22&partnerID=40&md5=1b38bb502d21b17e75cbbb4e7c72dbc4},
doi = {10.1007/978-0-387-73798-0_22},
issn = {15715736},
year  = {2007},
date = {2007-01-01},
journal = {IFIP International Federation for Information Processing},
volume = {243},
pages = {217-224},
abstract = {Legal doctrine starts to speak of Inter-systemic electronic contracting, where an important role is played by soft bots, i.e., intelligent software agents, which may be fiction as tools controlled by humans or faced as subjects of electronic commerce, or even seen as legal objects or as legal subjects. The use of software agents in electronic commerce scenarios must be connected with the existence of corporate bodies and Virtual Organizations. The issue to be discussed here is whether there should be Commercial Corporations for the use of Software Agents (as mere tools of the companies) or if the agents themselves can be seen as full and active participants in new types of commercial corporations and Virtual Organizations. © 2007 Springer Science+Business Media, LLC.},
note = {cited By 8},
keywords = {Commerce; Electronic commerce; Intelligent agents; Intelligent virtual agents; Societies and institutions; Virtual corporation; Electronic commerce; Virtual corporation, Electronic contracting; Intelligent software agent; Virtual organization; Soft-bots, Software agents; Software agents},
pubstate = {published},
tppubtype = {article}
}
@article{Abelha2007461,
title = {Ambient intelligence and simulation in health care virtual scenarios},
author = {A. Abelha and C. Analide and J. Machado and J. Neves and M. Santos and P. Novais},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548850597&doi=10.1007%2f978-0-387-73798-0_49&partnerID=40&md5=c446b6e4d373356254a000590ba81f08},
doi = {10.1007/978-0-387-73798-0_49},
issn = {15715736},
year  = {2007},
date = {2007-01-01},
journal = {IFIP International Federation for Information Processing},
volume = {243},
pages = {461-468},
abstract = {The success of change depends greatly on the ability to respond to human needs and to bridge the gap between humans and machines, and understanding the environment. With such experience, in addition to extensive practice in managing change, knowledge sharing and innovation, it would be interesting in offering a contribution by facilitating a dialogue, knowledge café (i.e. bringing in knowledge) on these issues, and how to apply them to new and altering scenarios. When one comes into the area of health care, one major limitation felt by those institutions is in the selection process of physicians to undertake a specific task, where there is a lack of objective, of validated measures of human performance. Indeed, objective measures are necessary if simulators are to be used to evaluate the skills and training of medical practitioners and teams or to evaluate the impact of new processes or equipment design on the overall system performance. In this paper it will be presented a logical theory of Situation Awareness (SA) and discusses the methods required for developing an objective measure of SA within the context of a simulated medical environment, as the one referred to above. Analysis and interpretation of SA data for both individual and team performance in health care are presented. © 2007 Springer Science+Business Media, LLC.},
note = {cited By 15},
keywords = {Ambient intelligence; Human performance; Knowledge-sharing; Managing changes; Medical practitioner; Objective measure; Situation awareness; Virtual scenario, Health care; Health care, Virtual corporation; Virtual corporation},
pubstate = {published},
tppubtype = {article}
}
@article{Machado2007151,
title = {Ambient intelligence via multiagent systems in the medical arena},
author = {J. Machado and V. Alves and A. Abelha and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-45549089672&partnerID=40&md5=8c3504f49cbc05a23bada4fe1bf5076a},
issn = {14728915},
year  = {2007},
date = {2007-01-01},
journal = {Engineering Intelligent Systems},
volume = {15},
number = {3},
pages = {151-158},
abstract = {Hospitals are complex work environments where people and information are distributed, thus demanding considerable coordination and communication among the professionals that work in such settings. Not only are they technology rich environments, but their workers experience a high level of mobility, resulting in information infrastructures with artifacts distributed throughout the premises. On the other hand, Hospital Information Systems that provide access to electronic patient records are a step in the direction of providing accurate and timely information to hospital staff in support of adequate decision-making. This has motivated the introduction of computing technology in hospitals based on designs which respond to their particular conditions and demands. The aim of this paper is, therefore, to present GENsis, an Agency for Integration, Archive and Diffusion of Medical Information, that configures a new form of Intelligence, the Ambient Intelligence one, that can be seen as a new paradigm in problem solving, once it empowers people through a digital environment that is aware of the users' context, and is sensitive and reactive to their needs. GENsis is like a symbiont, with a close association with core applications, namely the Picture Archive Communication System, the Radiological Information System and the Electronic Medical Record Information System, that are built upon pro-active agents that communicate through message passing. © 2007 CRL Publishing Ltd.},
note = {cited By 27},
keywords = {(I, Information systems},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Machado2007548,
title = {Formal models in Web based contracting},
author = {J. Machado and F. Andrade and J. Neves and P. Novais and C. Analide},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250776977&doi=10.1109%2fWI-IATW.2006.74&partnerID=40&md5=74d0aeb2f8a60e1810267059ac5f6e81},
doi = {10.1109/WI-IATW.2006.74},
isbn = {0769527493; 9780769527499},
year  = {2007},
date = {2007-01-01},
journal = {Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops Proceedings)},
pages = {548-551},
address = {Hong Kong},
abstract = {Legal principles have some difficulty to deal with software agents celebrating contracts and operating in e-commerce environments without direct human intervention. Autonomous intelligent agents have a control on their own actions and states, supporting or taking effective decisions. Therefore, some qualitative parameters such as trust, reputation and quality of information have to be taken under consideration to evaluate, certify and justify such decisions. Indeed, this paper shows how to construct a dynamic virtual world of complex and interacting entities or agents, organized in terms of Multi-Agent Systems (MAS), that compete against one another in order to solve a particular problem, according to a rigorous selection regime in which its fitness is judged by one criterion alone, a measure of the quality of information of the agent or agents, here understood as evolutionary logic theories. This virtual world could witness the emergence of our first learning, thinking machines, that may cater for some issues on the evolution of formal models of the world in general, and on what is concerned with the objectives set to this work, in contracting, and foray into a vast, untapped technological market. © 2006 IEEE.},
note = {cited By 3; Conference of 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology ; Conference Date: 18 December 2006 Through 22 December 2006; Conference Code:69803},
keywords = {Autonomous agents; Contracts; Decision support systems; Financial data processing; Intelligent agents; Multi agent systems, Autonomous intelligent agents; Evolutionary logic theories; Formal models, Electronic commerce},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Costa2007200,
title = {Inter-organization cooperation for care of the elderly},
author = {R. Costa and P. Novais and J. Machado and C. Alberto and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902327473&doi=10.1007%2f978-0-387-75494-9_25&partnerID=40&md5=94d878cda32dc7ff26eed5e8ae30ef28},
doi = {10.1007/978-0-387-75494-9_25},
issn = {18684238},
year  = {2007},
date = {2007-01-01},
journal = {IFIP Advances in Information and Communication Technology},
volume = {252 VOLUME 2},
pages = {200-208},
publisher = {Springer New York LLC},
address = {Wuhan},
abstract = {With the growing numbers of the elderly population, the society is face to face with a set of new problems, namely the lack of resources to assist their living in a noble mode. Nevertheless, with the use of new computational technologies and novel methodologies for problem solving, some solutions to these problems are emerging (e.g., remote sensing/assistance/supervision). Therefore, it is our goal to show that under such scenarios, it is possible to bring into play different interconnected virtual organizations, through which will be provided to the population, in general, and the elderly, in particular, a number of services (e.g., healthcare, entertainment, learning), without derealization or messing up with their routine. © 2007 by International Federation for Information Processing.},
note = {cited By 12; Conference of 7th IFIP International Conference on e-Business, e-Services, and e-Society, I3E 2007 ; Conference Date: 10 October 2007 Through 12 October 2007; Conference Code:105471},
keywords = {Computational technology; Elderly populations; Face to face; Inter-organization; Novel methodology; Number of services; Virtual organization, Electronic commerce; Remote sensing; World Wide Web, Remote sensing; Problem solving},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marreiros2007394,
title = {Modelling group decision simulation through argumentation},
author = {G. Marreiros and P. Novais and J. MacHado and C. Ramos and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898430569&partnerID=40&md5=b4c3fe69cde9c209961008037eb0eb9e},
year  = {2007},
date = {2007-01-01},
journal = {ESM 2007 - 2007 European Simulation and Modelling Conference: Modelling and Simulation 2007},
pages = {394-401},
publisher = {EUROSIS},
address = {St. Julians},
abstract = {Group decision making plays an important role in today's organisations. The impact of decision making is so high and complex, that rarely the decision making process is made individually. In Group Decision Argumentation, there is a set of participants, with different profiles and expertise levels, that exchange ideas or engage in a process of argumentation and counter- argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. In this paper, it is proposed a Multi-Agent simulator for the behaviour representation of group members in a decision making process. Agents behave depending on rational and emotional intelligence and use persuasive argumentation to convince and make alternative choices.},
note = {cited By 0; Conference of 21st Annual European Simulation and Modelling Conference, ESM 2007 ; Conference Date: 22 October 2007 Through 24 October 2007; Conference Code:104354},
keywords = {Argumentation; Decision making process; Emotional intelligence; Group decision; Group Decision Making; Group members, Decision making, Modal analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Costa2007323,
title = {Intelligent mixed reality for the creation of ambient assisted living},
author = {R. Costa and J. Neves and P. Novais and J. Machado and L. Lima and C. Alberto},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38349054346&doi=10.1007%2f978-3-540-77002-2_27&partnerID=40&md5=c898b11a127b0edf983079732f8279b2},
doi = {10.1007/978-3-540-77002-2_27},
issn = {03029743},
year  = {2007},
date = {2007-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {4874 LNAI},
pages = {323-331},
publisher = {Springer Verlag},
address = {Guimaraes},
abstract = {Demographical and social changes have an enormous effect on health care, emergency and welfare services. Indeed, as the average age continues to rise, it is set the mood to an exponential growth in assistance and care, resulting in higher service costs, a decrease in quality of service, or even both. On the other hand, as part of the evolution of traditional Virtual Reality Environments (or Intelligent Mixed Reality), a striving expression for Ambient Intelligence (Ami) it is possible to outline the role of AmI in healthcare, by focusing on its technological, logical (relational) and common sense nature. Our goal is to have in place an electronically-based monitoring system. This would reduce response time to adverse events, improve analytics and reporting, and will provide caregivers with the information they need to positively impact the care of individual patients. © Springer-Verlag Berlin Heidelberg 2007.},
note = {cited By 20; Conference of 13th Portuguese Conference on Artificial Intelligence, EPIA 2007 Workshops ; Conference Date: 3 December 2007 Through 7 December 2007; Conference Code:71232},
keywords = {Ambient assisted living; Ambient intelligence; Collaborative networks; Exponential growth, Decision trees; Health care; Information systems; Patient monitoring; Social aspects; Virtual reality, Intelligent agents},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neves2007160,
title = {The halt condition in genetic programming},
author = {J. Neves and J. Machado and C. Analide and A. Abelha and L. Brito},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38349047757&doi=10.1007%2f978-3-540-77002-2_14&partnerID=40&md5=78704ec4b5e25b26ee349c86514b2cf3},
doi = {10.1007/978-3-540-77002-2_14},
issn = {03029743},
year  = {2007},
date = {2007-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {4874 LNAI},
pages = {160-169},
publisher = {Springer Verlag},
address = {Guimaraes},
abstract = {In this paper we address the role of divergence and convergence in creative processes, and argue about the need to consider them in Computational Creativity research in the Genetic or Evolutionary Programming paradigm, being one's goal the problem of the Halt Condition in Genetic Programming. Here the candidate solutions are seen as evolutionary logic programs or theories, being the test whether a solution is optimal based on a measure of the quality-of-information carried out by those logical theories or programs. Furthermore, we present Conceptual Blending Theory as being a promising framework for implementing convergence methods within creativity programs, in terms of the logic programming framework. © Springer-Verlag Berlin Heidelberg 2007.},
note = {cited By 80; Conference of 13th Portuguese Conference on Artificial Intelligence, EPIA 2007 Workshops ; Conference Date: 3 December 2007 Through 7 December 2007; Conference Code:71232},
keywords = {Computational complexity; Convergence of numerical methods; Evolutionary algorithms; Logic programming; Problem solving, Computational creativity; Conceptual blending theory; Quality of information, Genetic programming},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marreiros2007309,
title = {Argumentation-based decision making in ambient intelligence environments},
author = {G. Marreiros and R. Santos and P. Novais and J. Machado and C. Ramos and J. Neves and J. Bula-Cruz},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38349032855&doi=10.1007%2f978-3-540-77002-2_26&partnerID=40&md5=abf9815010f8a6bea34919d2a1971707},
doi = {10.1007/978-3-540-77002-2_26},
issn = {03029743},
year  = {2007},
date = {2007-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {4874 LNAI},
pages = {309-322},
publisher = {Springer Verlag},
address = {Guimaraes},
abstract = {In Group Decision Making argumentation has a crucial role; we have a set of participants, with different points of view that exchange ideas or engage in a process of argumentation and counter-argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. In this paper we propose an argumentation-based system, where intelligent agents simulates the behaviour of individuals as members of a group in a decision making process. Our agents operate in ambience intelligent environments and behave depending on rational and emotional factors. © Springer-Verlag Berlin Heidelberg 2007.},
note = {cited By 9; Conference of 13th Portuguese Conference on Artificial Intelligence, EPIA 2007 Workshops ; Conference Date: 3 December 2007 Through 7 December 2007; Conference Code:71232},
keywords = {Argumentation; Artificial societies; Group decision making, Decision making, Intelligent agents; Multi agent systems; Problem solving},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marreiros2006263,
title = {An agent-based approach to group decision simulation using argumentation},
author = {G. Marreiros and P. Novais and J. Machado and C. Ramos and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-83755172034&partnerID=40&md5=5460bc0d65b19e9889dd2c57db2698df},
year  = {2006},
date = {2006-01-01},
journal = {Proceedings of the International Multiconference on Computer Science and Information Technology, IMCSIT},
volume = {1},
pages = {263-270},
address = {Wisla},
abstract = {Group decision making simulation allows for the creation of virtual group decision scenarios. The use of a group decision simulator enhances user competences in this area, to test different argumentation strategies and to validate "what if" useful real world scenarios. In this paper, it is proposed a multi-agent model to simulate group decision making tasks. Agents are designed with emotional properties, reason with incomplete information and use persuasive argumentation to convince the other group elements about the best alternative choice. © 2006 PIPS.},
note = {cited By 8; Conference of 1st International Multiconference on Computer Science and Information Technology, IMCSIT 2006. Part of 22nd Autumn Meeting of Polish Information Processing Society, PIPS ; Conference Date: 6 November 2006 Through 10 November 2006; Conference Code:87733},
keywords = {Agent-based approach; Group decision; Group Decision Making; Incomplete information; Multi-Agent Model; Real-world scenario; Virtual group, Computer science; Data processing; Decision making; Information technology, Computer simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Machado200694,
title = {Ambient intelligence in medicine},
author = {J. Machado and A. Abelha and J. Neves and M. Santos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-52949086819&doi=10.1109%2fBIOCAS.2006.4600316&partnerID=40&md5=51e7b7118fc18cf6ef71eb64d03a7c8f},
doi = {10.1109/BIOCAS.2006.4600316},
isbn = {1424404371; 9781424404377},
year  = {2006},
date = {2006-01-01},
journal = {IEEE 2006 Biomedical Circuits and Systems Conference Healthcare Technology, BioCAS 2006},
pages = {94-97},
address = {London},
abstract = {The Electronic Medical Record (EMR) is a core application which covers horizontally the health care unit and makes possible a transverse analysis of medical records along the services, units or treated pathologies, bringing to the healthcare arena new methodologies for problem solving, computational models, technologies and tools. One aims to develop a comprehensive, structured approach to EMR development and analysis. Indeed, this paper will thrash out the inner features of intelligent agents to be used in the EMR, in the context of the Telemedical Information Society, a step in the direction of Intelligent Health Care Units. © 2006 IEEE.},
note = {cited By 25; Conference of IEEE 2006 Biomedical Circuits and Systems Conference Healthcare Technology, BioCAS 2006 ; Conference Date: 29 November 2006 Through 1 December 2006; Conference Code:73754},
keywords = {Ambient Intelligence; Biomedical circuits and systems; Computational modelling; Electronic medical record; Health care technology; Information society; Medical records; Structured approach, Artificial intelligence; Computer software; Decision making; Electric relays; Electronic medical equipment; Health care; Intelligent agents; Medical computing; Medicine; Problem solving; Technology, Health},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Marreiros2006310,
title = {Emotions on agent based simulators for group formation},
author = {G. Marreiros and P. Novais and J. Machado and C. Ramos and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898403365&partnerID=40&md5=06d13be1aedd3763d8f9442f26c1a190},
year  = {2006},
date = {2006-01-01},
journal = {ESM 2006 - 2006 European Simulation and Modelling Conference: Modelling and Simulation 2006},
pages = {310-314},
publisher = {EUROSIS},
address = {Toulouse},
abstract = {Time and space consuming are key factors in a meeting, and therefore must be object of consideration in any process of socialization. So, group decision simulation could be a valuable training tool, through which it will be possible to create and test virtual group decision scenarios. In this work we propose a multi-agent simulator of group decision making that models the participant cortex by considering its emotional states and the exchange of arguments among them.},
note = {cited By 3; Conference of 20th European Simulation and Modelling Conference, ESM 2006 ; Conference Date: 23 October 2006 Through 25 October 2006; Conference Code:104353},
keywords = {Agent based; Emotional state; Group decision; Group Decision Making; Group formations; Valuable training; Virtual group, Agents; Decision making, Modal analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
@inbook{Analide2005436,
title = {Quality of knowledge in virtual entities},
author = {C. Analide and P. Novais and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898238585&doi=10.4018%2f978-1-59140-556-6.ch073&partnerID=40&md5=5e74d740ca1b6ab2c5ca62412640d5c3},
doi = {10.4018/978-1-59140-556-6.ch073},
isbn = {9781591405566},
year  = {2005},
date = {2005-01-01},
journal = {Encyclopedia of Communities of Practice in Information and Knowledge Management},
pages = {436-442},
publisher = {IGI Global},
note = {cited By 26},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Andrade2005503,
title = {Legal security and credibility in agent based virtual enterprises},
author = {F. Andrade and J. Neves and P. Novais and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902774867&doi=10.1007%2f0-387-29360-4_53&partnerID=40&md5=f5002c3862c1cc16529edd766fddb829},
doi = {10.1007/0-387-29360-4_53},
issn = {18684238},
year  = {2005},
date = {2005-01-01},
journal = {IFIP Advances in Information and Communication Technology},
volume = {186},
pages = {503-512},
publisher = {Springer Science and Business Media, LLC},
address = {Valencia},
abstract = {Recent trends in the field of Artificial Intelligence, brought along new ways of formalizing and expressing wills and declarations. Its application to Virtual Enterprises requires an analysis of the interactions among agents, frameworks and users, as well as technical and legal analysis, in order to discover the rules to be applied, to solve a particular problem under a prospective scenario. Credibility, trust and security issues must be taken under consideration, especially concerning authenticity, confidentiality, integrity and nonrepudiation. In order to increase the use of agents in Virtual Enterprises, besides the analysis and research of legal solutions in the commercial arena, it is essential to assure that agents will meet requirements of credibility and trust, insuring a transparent and secure way for their commercial acting, now capable of generating legal relations. This paper shows how to construct a dynamic virtual world of complex and interacting entities or agents, in which fitness is judged by a quality of information criterion. © 2005 by International Federation for Information Processing.},
note = {cited By 19; Conference of IFIP TC5 WG 5.5 6th IFIP Working Conference on Virtual Enterprises, PRO-VE 2005 ; Conference Date: 26 September 2005 Through 28 September 2005; Conference Code:105830},
keywords = {Artificial intelligence, Interacting entities; ITS applications; Legal solutions; Non-repudiation; Quality of information; Trust and security; Virtual enterprise; Virtual worlds, Virtual corporation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Machado2005199,
title = {Web-based simulation in medicine},
author = {J. Machado and V. Alves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898486575&partnerID=40&md5=2da0464605f7eb857b74ff08e42a4773},
year  = {2005},
date = {2005-01-01},
journal = {2005 European Simulation and Modelling Conference, ESM 2005 - Proceedings},
pages = {199-201},
publisher = {EUROSIS},
address = {Porto},
abstract = {This paper describes some particularities of an e-learning system that is being developed to simulate conversational dialogue in the area of Medicine, that enables the integration of highly heterogeneous sources of information into a coherent knowledge base, either from the tutor's point of view or the development of the discipline in itself, i.e. the system's content is created automatically by the physicians as their daily work goes on. This will encourage students to articulate lengthier answers that exhibit deep reasoning, rather than to deliver straight tips of shallow knowledge. The goal is to take advantage of the normal functioning of health care units to build on the fly a knowledge base of cases and data for teaching and research purposes.},
note = {cited By 5; Conference of 19th Annual European Simulation and Modelling Conference, ESM 2005 ; Conference Date: 24 October 2005 Through 26 October 2005; Conference Code:104352},
keywords = {E-learning, E-learning systems; Educational aid; Heterogeneous sources; Knowledge base; On the flies; Teaching and researches; Web-based simulations, Knowledge based systems; Modal analysis; Multi agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Andrade2004123,
title = {Software agents as legal persons},
author = {F. Andrade and J. Neves and P. Novais and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902474435&doi=10.1007%2f1-4020-8139-1_14&partnerID=40&md5=d6a2dc67a3a371fc5c1d4f8a09c5ea68},
doi = {10.1007/1-4020-8139-1_14},
issn = {18684238},
year  = {2004},
date = {2004-01-01},
journal = {IFIP Advances in Information and Communication Technology},
volume = {149},
pages = {123-131},
publisher = {Springer New York LLC},
address = {Toulouse},
abstract = {The Law has long been recognizing that, besides natural persons, other entities socially engaged must also be subject of rights and obligations. Western laws usually recognize Corporate Bodies as having legal personality and capacity for every right and obligation needed or convenient to the prosecution of its social goals. But can we foresee a similar attribution of such a regime to software agents? In other words, are intelligent software agents capable of being personified? One of the main characteristics of a personality is the existence of a physical being or organization provided with its own will. In that sense, intelligent software agents are quite close to human beings. Indeed, they have a physical existence, and they have the capability of learning and of having a will of their own. © 2004 Springer Science + Business Media, Inc.},
note = {cited By 6; Conference of IFIP TC5/WG5.5 5th Working Conference on Virtual Enterprises, PRO-VE 2004 ; Conference Date: 22 August 2004 Through 27 August 2004; Conference Code:105745},
keywords = {Human being; Intelligent software agent; Legal personality; Social goals, Software agents, Virtual corporation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Novais2003703,
title = {Argumentative procedures in e-commerce environments},
author = {P. Novais and J. Neves and L. Brito and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904245828&partnerID=40&md5=004f5c300f3aa77a530321ba2950390b},
issn = {18684238},
year  = {2003},
date = {2003-01-01},
journal = {IFIP Advances in Information and Communication Technology},
volume = {105},
pages = {703-716},
publisher = {Springer New York LLC},
address = {Lisbon},
abstract = {The use of agent's technology in electronic commerce environments leads to the necessity to introduce some sort of formal attitude in the processes of software development and analysis. Logic programming and specially extended logic programming provides a powerful tool for achieving such goal; i.e., besides being mathematically correct it makes the prototyping phase easier. However, although no simple methodology had yet been stated to address the complexity present in this approach, it not only addresses problems of architecture development and analysis, but also looks at problems of knowledge representation and reasoning, and machine learning. Such a framework will follow the Experience-Based Mediator agent paradigm particularly suited to take into account the argumentation schemes that are inherent to any electronic commerce deal. © 2003 by Springer Science+Business Media New York.},
note = {cited By 1; Conference of 2nd IFIP Conference on E-Commerce, E-Business, E-Government, 13E 2002 ; Conference Date: 7 October 2002 Through 9 October 2002; Conference Code:106294},
keywords = {Argument-based negotiation; Argumentation schemes; Extended logic programming; Knowledge representation and reasoning; Mediator agents, Computer circuits; e-government; Formal methods; Intelligent agents; Knowledge representation; Logic programming; Software design, Electronic commerce},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neves199968,
title = {A unified framework for data modeling on medical information systems},
author = {J. Neves and P. Cortez and M. Rocha and A. Abelha and J. MacHado and V. Alves and S. Basto and H. Botelho and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033251335&doi=10.3233%2f978-1-60750-912-7-68&partnerID=40&md5=404bff3ecb8e5585fe3c1c9fdd1459a6},
doi = {10.3233/978-1-60750-912-7-68},
issn = {09269630},
year  = {1999},
date = {1999-01-01},
journal = {Studies in Health Technology and Informatics},
volume = {68},
pages = {68-71},
publisher = {IOS Press},
address = {Ljubljana},
abstract = {Medical Information Systems (MIS) are seen as a way of optimizing the use of existing health-care infrastructure, without resorting to new and costly hospital (re)construction. The qualitative (re)design of such an environment requires a basic understanding of patient and doctors related characteristics and capabilities. Patient care, patient education, medical education, and clinical research need to be considered to meet the basic requirements on the level of services desirable, determined on the basis of the patient's length of stay; i.e., used for modeling the significant entities of such a world. The aim is to extract conclusions for the level of services provided to the users. One's concept will capture, as well as will integrate, the basic design principles under which MIS may be set.},
note = {cited By 0; Conference of 15th Congress on Medical Informatics Europe, MIE 1999 ; Conference Date: 22 August 1999 Through 26 August 1999},
keywords = {article; artificial intelligence; computer assisted diagnosis; computer assisted therapy; computer program; computer simulation; expert system; human; medical informatics, Artificial Intelligence; Computer Simulation; Diagnosis, Basic designs; Length of stay; Level of Service; Patient care; Patient education; Unified framework, Bioinformatics; Clinical research; Information use; Medical computing; Medical education, Computer-Assisted, Computer-Assisted; Expert Systems; Humans; Medical Informatics Computing; Software; Therapy, Medical information systems},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neves1998159,
title = {Extended logic programming applied to the specification of multi-agent systems and their computing environments},
author = {Jose Neves and Jose Machado and Cesar Analide and Paulo Novais and Antonio Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031673874&partnerID=40&md5=08133e65f4f0c50b4fa9b7a825c799c4},
year  = {1998},
date = {1998-01-01},
journal = {Proceedings of the IEEE International Conference on Intelligent Processing Systems, ICIPS},
volume = {1},
pages = {159-164},
publisher = {IEEE, Piscataway, NJ, United States},
address = {Beijing, China},
abstract = {In this work it is presented a logical framework to model some aspects of contextuality; i.e. generating contexts in a multi-context setting. Following the existing work on Extended Logic Programming and Multi-Agent Systems, a contextual reasoning procedure for a particular class of multi-context systems, the law ones, is proposed based on the Grice's maxims, which in turn are used to support a larger set of contexts by combining contexts into compound structures, thus defining a logic of contexts. The notion of compound contexts reflects the beliefs, desires, intentions and obligations, among others, that depend on the problem, leading to a variety of dynamic context formations. In its applied form, it will be considered the Portuguese Public Prosecution Service, which is the state body entrusted with representing the state, bringing criminal cases to court, defending democratic legality, and any other interests that The Law determines.},
note = {cited By 6; Conference of Proceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, ICIPS'97. Part 1 (of 2) ; Conference Date: 28 October 1997 Through 31 October 1997; Conference Code:48321},
keywords = {Artificial intelligence; Decision support systems; Laws and legislation, Contextual reasoning; Extended logic programming; Grice's maxims; Multi agent systems, Logic programming},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Neves1997256,
title = {Application of artificial intelligence to law enforcement},
author = {Jose Neves and Jose Machado and Antonio Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0030683086&partnerID=40&md5=39b557e4ae2be634a7e33c062091971e},
year  = {1997},
date = {1997-01-01},
journal = {Proceedings of the International Conference on Artificial Intelligence and Law},
pages = {256},
publisher = {ACM, New York, NY, United States},
address = {Melbourne, Aust},
abstract = {The area of information retrieval is concerned, in particular, with selecting documents from a collection that will be of interest to an user with a stated information need or question. Although it remains an area of considerable theoretical and practical importance, in general it comes to be critical when one considers the organization and functioning of a Public Prosecution Service, in terms of its administrative procedures, service distribution or the application of The Law. This article describes a part of a project, the MAD's one, A Decision Support System for Legal Reasoning, with mention to the architecture and the formal model for retrieval and update of legal documents, in use at the Portuguese Public Prosecution Service. It combines agent's based technology, disjunctive logic programming to the representation of partial information, and inference based semantic nets.},
note = {cited By 0; Conference of Proceedings of the 1997 6th International Conference on Artificial Intelligence and Law ; Conference Date: 30 June 1997 Through 3 July 1997; Conference Code:47164},
keywords = {Abstract only; Inference based semantic nets; Law enforcement, Artificial intelligence, Computational linguistics; Computer architecture; Decision support systems; Inference engines; Information retrieval; Laws and legislation; Logic programming; Neural networks},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Cortez19952689,
title = {Neural network based time series forecasting system},
author = {Paulo Cortez and Miguel Rocha and Jose Machado and Jose Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029452555&partnerID=40&md5=547210bf629ba7964e810106e283214a},
year  = {1995},
date = {1995-01-01},
journal = {IEEE International Conference on Neural Networks - Conference Proceedings},
volume = {5},
pages = {2689-2693},
publisher = {IEEE, Piscataway, NJ, United States},
address = {Perth, Aust},
abstract = {The Neural Network (NN) arena has suffered in the past years a remarkable development as one of the novel fields for Artificial Intelligence (AI). Time Series Analysis (TSA) based on models of the variability of observations by postulating trends and cyclic effects, with a view to understand the cause of variation and to improve forecasting, suggests the use of NNs that do something like, what is called in statistics, Principal Component Analysis (PCA). The purpose of this work is to present a logical based NN system, along with: (i) Time Series Forecasting (TSF), with its characteristics of strong noise component and non-linearity in data, showing itself as a field in which the use of NN's stuff is particularly advisable; (ii) PCA rules, organized in a default hierarchy as logical theories, competing with one another for the right to represent a particular situation or to predict its successors; i.e., assisting in the process of choosing the best network to forecast each series. Some trials will be conducted, and the basic performance measures used as baselines for comparison with other methods.},
note = {cited By 9; Conference of Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) ; Conference Date: 27 November 1995 Through 1 December 1995; Conference Code:44687},
keywords = {Logic programming; Mathematical models; PROLOG (programming language); Time series analysis, Neural networks, Time series forecasting system},
pubstate = {published},
tppubtype = {inproceedings}
}