AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia

Detalhes bibliográficos
Autor(a) principal: Elvas, Luís B.
Data de Publicação: 2023
Outros Autores: Nunes, Miguel, Ferreira, Joao C., Dias, Miguel Sales, Rosario, Luis
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/59885
Resumo: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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spelling AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemiaAortic stenosisArtificial intelligenceCardiovascular diseasesData miningExploratory data analysisMachine learningMyocardial infarctionPredictionPulmonary thromboembolismStenosis cardiology© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.This work was partially funded by national funds through FCT—Fundação para a Ciência e Tecnologia, I.P., under the projects FCT UIDB/04466/2020, and FCT DSAIPA/AI/0122/2020 AIMHealth—Mobile Applications Based on Artificial Intelligence. This research also received funding from ERAMUS+ project NEMM with grant 101083048. During the development of this work, Luís Elvas was a Ph.D. grant holder, funded by FCT with UI/BD/151494/2021.MDPIRepositório da Universidade de LisboaElvas, Luís B.Nunes, MiguelFerreira, Joao C.Dias, Miguel SalesRosario, Luis2023-10-18T14:20:34Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/59885engJ Pers Med. 2023 Sep 21;13(9):142110.3390/jpm130914212075-4426info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:08:56Zoai:repositorio.ul.pt:10451/59885Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:09:33.826389Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia
title AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia
spellingShingle AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia
Elvas, Luís B.
Aortic stenosis
Artificial intelligence
Cardiovascular diseases
Data mining
Exploratory data analysis
Machine learning
Myocardial infarction
Prediction
Pulmonary thromboembolism
Stenosis cardiology
title_short AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia
title_full AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia
title_fullStr AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia
title_full_unstemmed AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia
title_sort AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia
author Elvas, Luís B.
author_facet Elvas, Luís B.
Nunes, Miguel
Ferreira, Joao C.
Dias, Miguel Sales
Rosario, Luis
author_role author
author2 Nunes, Miguel
Ferreira, Joao C.
Dias, Miguel Sales
Rosario, Luis
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Elvas, Luís B.
Nunes, Miguel
Ferreira, Joao C.
Dias, Miguel Sales
Rosario, Luis
dc.subject.por.fl_str_mv Aortic stenosis
Artificial intelligence
Cardiovascular diseases
Data mining
Exploratory data analysis
Machine learning
Myocardial infarction
Prediction
Pulmonary thromboembolism
Stenosis cardiology
topic Aortic stenosis
Artificial intelligence
Cardiovascular diseases
Data mining
Exploratory data analysis
Machine learning
Myocardial infarction
Prediction
Pulmonary thromboembolism
Stenosis cardiology
description © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
publishDate 2023
dc.date.none.fl_str_mv 2023-10-18T14:20:34Z
2023
2023-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/59885
url http://hdl.handle.net/10451/59885
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv J Pers Med. 2023 Sep 21;13(9):1421
10.3390/jpm13091421
2075-4426
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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