Article
Journal Articles
2024
Miranda R; Alves C; Sousa R; Chaves A; Montenegro L; Peixoto H; Durães D; Machado R; Abelha A; Novais P; Machado J
Revolutionising the Quality of Life: The Role of Real-Time Sensing in Smart Cities Journal Article
Em: Electronics (Switzerland), vol. 13, não 3, 2024, ISSN: 20799292.
Resumo | BibTeX | Altmetric | Links:
@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}
}
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.
Silva A C; Machado J; Sampaio P
Predictive quality model for customer defects Journal Article
Em: TQM Journal, vol. 36, não 9, pp. 155-174, 2024, ISSN: 17542731, (cited By 0).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {article}
}
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.
Nguyen H; Pham V; Ngo H Q; Huynh A; Nguyen B; Machado J
Intelligent search system for resume and labor law Journal Article
Em: PeerJ Computer Science, vol. 10, 2024, ISSN: 23765992, (cited By 0).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {article}
}
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.
2023
Gonçalves F; Silva G O; Santos A; Rocha A M A C; Peixoto H; Durães D; Machado J
Urban Traffic Simulation Using Mobility Patterns Synthesized from Real Sensors Journal Article
Em: Electronics (Switzerland), vol. 12, não 24, 2023, ISSN: 20799292, (cited By 2).
Resumo | BibTeX | Altmetric | Links:
@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}
}
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.
Silva A L; Oliveira P; Durães D; Fernandes D; Névoa R; Monteiro J; Melo-Pinto P; Machado J; Novais P
A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications Journal Article
Em: Sensors, vol. 23, não 14, 2023, ISSN: 14248220, (cited By 1).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {article}
}
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.
Machado J M; Chamoso P; Hernández G; Bocewicz G; Loukanova R; Jove E; Rey A M D; Ricca M
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) Journal Article
Em: Lecture Notes in Networks and Systems, vol. 585, pp. C1, 2023, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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}
}
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.
Machado J; Rodrigues C; Sousa R; Gomes L M
Drug–drug interaction extraction-based system: An natural language processing approach Journal Article
Em: Expert Systems, 2023, ISSN: 02664720, (cited By 2).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {article}
}
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.
2022
Oliveira D; Ferreira D; Abreu N; Leuschner P; Abelha A; Machado J
Prediction of COVID-19 diagnosis based on openEHR artefacts Journal Article
Em: Scientific Reports, vol. 12, não 1, 2022, ISSN: 20452322, (cited By 6).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {article}
}
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).
Montenegro L; Abreu M; Fred A; Machado J M
Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning Journal Article
Em: Applied Sciences (Switzerland), vol. 12, não 15, 2022, ISSN: 20763417, (cited By 6).
Resumo | BibTeX | Altmetric | Links:
@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}
}
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
Ferreira D; Neto C; Lopes J; Duarte J; Abelha A; Machado J
Predicting the Survival of Primary Biliary Cholangitis Patients Journal Article
Em: Applied Sciences (Switzerland), vol. 12, não 16, 2022, ISSN: 20763417, (cited By 1).
Resumo | BibTeX | Altmetric | Links:
@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}
}
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.
Brito C; Esteves M; Peixoto H; Abelha A; Machado J
A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis Journal Article
Em: Wireless Networks, vol. 28, não 3, pp. 1269-1277, 2022, ISSN: 10220038, (cited By 6).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {article}
}
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.
Esteves M; Abelha A; Machado J
The development of a pervasive Web application to alert patients based on business intelligence clinical indicators: a case study in a health institution Journal Article
Em: Wireless Networks, vol. 28, não 3, pp. 1279-1285, 2022, ISSN: 10220038, (cited By 4).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {article}
}
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.
Reinolds F; Neto C; Machado J
Deep Learning for Activity Recognition Using Audio and Video Journal Article
Em: Electronics (Switzerland), vol. 11, não 5, 2022, ISSN: 20799292, (cited By 10).
Resumo | BibTeX | Altmetric | Links:
@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}
}
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.
Santos F; Durães D; Marcondes F S; Hammerschmidt N; Machado J; Novais P
Weakness Evaluation on In-Vehicle Violence Detection: An Assessment of X3D, C2D and I3D against FGSM and PGD Journal Article
Em: Electronics (Switzerland), vol. 11, não 6, 2022, ISSN: 20799292, (cited By 1).
Resumo | BibTeX | Altmetric | Links:
@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}
}
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.
Miranda F; Sousa A R; Duarte J; Abelha A C; Machado J
Machine Learning Applied to Health Information Exchange Journal Article
Em: International Journal of Reliable and Quality E-Healthcare, vol. 11, não 1, 2022, ISSN: 21609551, (cited By 1).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {article}
}
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.
Proceedings Articles
2024
Sousa R; Ribeiro C; Cardoso C; Freixo B; Peixoto H; Abelha A; Machado J
The Interplay of Inflation, Healthcare Spending, and Suicide Rates: An Empirical Analysis Proceedings Article
Em: A., Ferras C. Diez J. H. Rocha (Ed.): pp. 467-476, Springer Science and Business Media Deutschland GmbH, 2024, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Alves C; Machado J
Immersive Shopping Experiences: The Role of Augmented Reality in E-Commerce Proceedings Article
Em: A., Ferras C. Diez J. H. Rocha (Ed.): pp. 205-213, Springer Science and Business Media Deutschland GmbH, 2024, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Chaves A; Sousa R; Machado J; Abelha A; Peixoto H
Collaborative Platform for Intelligent Monitoring of Diabetic Foot Patients - Colab4IMDF Proceedings Article
Em: A., Ferras C. Diez J. H. Rocha (Ed.): pp. 195-204, Springer Science and Business Media Deutschland GmbH, 2024, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Sousa R; Peixoto H; Guimarães T; Abelha A; Machado J
Towards a Standardized Real-Time Data Repository based on Laboratory Test Results Proceedings Article
Em: E., Shakshuki (Ed.): pp. 452-457, Elsevier B.V., 2024, ISSN: 18770509, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Cruz G; Guimarães T; Santos M F; Machado J
Decentralize Healthcare Marketplace Proceedings Article
Em: E., Shakshuki (Ed.): pp. 439-444, Elsevier B.V., 2024, ISSN: 18770509, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Alves C; Machado J; Reis J L
Review for Augmented Reality Shopping Application for Mobile Systems Proceedings Article
Em: dos Santos J.P. Reis J.L., Del Rio Araujo M. (Ed.): pp. 623-634, Springer Science and Business Media Deutschland GmbH, 2024, ISSN: 21903018, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
2023
Montenegro L; Gomes L M; Machado J M
AI-Based Medical Scribe to Support Clinical Consultations: A Proposed System Architecture Proceedings Article
Em: N., Vale Z. Moniz N. Moniz (Ed.): pp. 274-285, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 03029743, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Denanti S P; Yunita I; Widarmanti T; Machado J M F
The Correlation of Headline News Sentiment and Stock Return during Dividend Period Proceedings Article
Em: Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 9798350328028, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Moya A; Zhinin-Vera L; Navarro E; Jaen J; Machado J
Clustering ABI Patients for a Customized Rehabilitation Process Proceedings Article
Em: J., Urzaiz G. Bravo (Ed.): pp. 217-228, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Zhinin-Vera L; Moya A; Navarro E; Jaen J; Machado J
A Reinforcement Learning Algorithm for Improving the Generation of Telerehabilitation Activities of ABI Patients Proceedings Article
Em: J., Urzaiz G. Bravo (Ed.): pp. 15-26, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Hernández G; González-Briones A; Machado J; Chamoso P; Novais P
A Machine Learning Approach to Evaluating the Relationship Between Dental Extraction and Craniofacial Growth in Adolescents Proceedings Article
Em: C., Bonsangue M. M. Anutariya (Ed.): pp. 300-313, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18650929, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Dias M; Sousa R; Duarte J; Peixoto H; Abelha A; Machado J
Enhancing Data Science Interoperability: An Innovative System for Managing OpenEHR Structures Proceedings Article
Em: C., Bonsangue M. M. Anutariya (Ed.): pp. 288-299, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18650929, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Fauzan D F; Fauzi R; Pratiwi O N; Machado J M F
Breast Cancer Detection on Histopathology Images Using Pre-trained Computer Vision Models Proceedings Article
Em: Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 9798350303414, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Arfilinia A; Andreswari R; Hamami F; Machado J M F
Multidimensional Sentiment Analysis of Tourism Object in DKI Jakarta, Banten, East Java, Central Java and West Java using Support Vector Machine Algorithm Proceedings Article
Em: Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 9798350303414, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Prema N I G N A A; Avilandi P N; Fathan ; Andreswari R; Machado J M F
Discovery of Hospital Billing Process in a Regional Hospital Using Process Mining Proceedings Article
Em: Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 9798350303414, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Chaves A; Montenegro L; Peixoto H; Abelha A; Gomes L; Machado J
Intelligent Systems in Healthcare: An Architecture Proposal Proceedings Article
Em: P., Hornos M. J. Julian Inglada V. Novais (Ed.): pp. 230-238, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Neto C; Ferreira D; Cunha H; Pires M; Marques S; Sousa R; Machado J
Recommendation of Medical Exams to Support Clinical Diagnosis Based on Patient’s Symptoms Proceedings Article
Em: J.M., Peixoto H. Machado (Ed.): pp. 91-100, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18678211, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Sousa R; Oliveira D; Hak F; Machado J
The Impact of Contingency Measures on the COVID-19 Reproduction Rate Proceedings Article
Em: J.M., Peixoto H. Machado (Ed.): pp. 28-37, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18678211, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Sousa R; Gomes J; Gomes J; Arcipreste M; Guimarães P; Oliveira D; Machado J
COVID-19 Cases and Their Impact on Global Air Traffic Proceedings Article
Em: J.M., Peixoto H. Machado (Ed.): pp. 16-27, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18678211, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Ramos V; Marques C; Peixoto H; Machado J
Information Technology Monitoring in Healthcare: A Case Study Proceedings Article
Em: A., Ibarra W. Ferras C. Rocha (Ed.): pp. 351-361, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Coelho E; Pimenta N; Peixoto H; Durães D; Melo-Pinto P; Alves V; Bandeira L; Machado J; Novais P
Multi-agent System for Multimodal Machine Learning Object Detection Proceedings Article
Em: de Pison F. J. Perez Garcia H. Garcia Bringas P., Martinez (Ed.): pp. 673-681, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 03029743, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Miranda R; Ribeiro E; Durães D; Peixoto H; Machado R; Abelha A; Machado J
Smart Cities Using Crowdsensing and Geoferenced Notifications Proceedings Article
Em: L.F., Cardona O. Isaza G. Castillo Ossa (Ed.): pp. 97-110, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Sousa R; Peixoto H; Abelha A; Machado J
Implementing a Software-as-a-Service Strategy in Healthcare Workflows Proceedings Article
Em: S., Analide C. Sitek P. Ossowski (Ed.): pp. 347-356, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Vaz L; Peixoto H; Duarte J; Alvarez C; Machado J
Enhancing Clinical Management of Bariatric Surgery Using Business Intelligence Proceedings Article
Em: E., Shakshuki (Ed.): pp. 850-855, Elsevier B.V., 2023, ISSN: 18770509, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Miranda R; Alves C; Abelha A; Machado J
Data Platforms for Real-time Insights in Healthcare: Systematic Review Proceedings Article
Em: E., Shakshuki (Ed.): pp. 826-831, Elsevier B.V., 2023, ISSN: 18770509, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Braga D; Oliveira D; Rosario R; Novais P; Machado J
An Architecture Proposal for Noncommunicable Diseases Prevention Proceedings Article
Em: E., Shakshuki (Ed.): pp. 820-825, Elsevier B.V., 2023, ISSN: 18770509, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Silva I; Ferreira D; Peixoto H; Machado J
A Data Acquisition and Consolidation System based on openEHR applied to Physical Medicine and Rehabilitation Proceedings Article
Em: E., Shakshuki (Ed.): pp. 844-849, Elsevier B.V., 2023, ISSN: 18770509, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Lopes J; Sousa R; Abelha A; Machado J
Big Data in Healthcare Institutions: An Architecture Proposal Proceedings Article
Em: R., Zeng D. Huang H. Hou (Ed.): pp. 297-311, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18678211, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Sousa R; Sousa R; Peixoto H; Machado J
Prediction Models Applied to Lung Cancer Using Data Mining Proceedings Article
Em: L., Badica C. Jander K. Braubach (Ed.): pp. 195-200, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 1860949X, (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).
Resumo | BibTeX | Altmetric | Links:
@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}
}
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.
Pimenta N; Sousa R; Peixoto H; Machado J
A Comprehensive Study on Personal and Medical Information to Predict Diabetes Proceedings Article
Em: S., Sitek P. Mehmood R. Omatu (Ed.): pp. 197-207, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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}
}
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.
2022
Neto C; Ferreira D; Ramos J; Cruz S; Oliveira J; Abelha A; Machado J
Prediction Models for Coronary Heart Disease Proceedings Article
Em: K., Yigitcanlar T. Omatu S. Matsui (Ed.): pp. 119-128, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 23673370, (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).
Resumo | BibTeX | Altmetric | Links:
@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}
}
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.
Trisuciana F M; Witarsyah D; Sutoyo E; Machado J M
Clustering of COVID-19 Vaccination Recipients in DKI Jakarta Using the K-Medoids Algorithm Proceedings Article
Em: Institute of Electrical and Electronics Engineers Inc., 2022, ISBN: 9781665463874, (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).
Resumo | BibTeX | Altmetric | Links:
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Livros
2023
Machado J M; Peixoto H
Preface Livro
Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18678211, (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).
BibTeX | Links:
@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}
}
Machado J M; Chamoso P; Hernández G; Bocewicz G; Loukanova R; Jove E; Rey A M D; Ricca M
Preface Livro
Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 23673370, (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).
BibTeX | Links:
@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}
}
2022
González S R; Machado J M; González-Briones A; Wikarek J; Loukanova R; Katranas G; Casado-Vara R
Preface Livro
Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 23673370, (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).
@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}
}