Article

@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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

pubstate = {published},

tppubtype = {inproceedings}

}