Machine learning prediction of mortality in Acute Myocardial Infarction

Detalhes bibliográficos
Autor(a) principal: Oliveira, Mariana
Data de Publicação: 2023
Outros Autores: Seringa, Joana, Pinto, Fausto José, Henriques, Roberto, Magalhães, Teresa
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/151915
Resumo: Oliveira, M., Seringa, J., Pinto, F. J., Henriques, R., & Magalhães, T. (2023). Machine learning prediction of mortality in Acute Myocardial Infarction. BMC Medical Informatics and Decision Making, 23(1), 1-16. [70]. https://doi.org/10.1186/s12911-023-02168-6. --- The present publication was funded by Fundação Ciência e Tecnologia, IP national support through CHRC (UIDP/04923/2020). The funding body did not played any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
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spelling Machine learning prediction of mortality in Acute Myocardial InfarctionMachine learningCardiovascular diseasesAcute Myocardial InfarctionPredictive modelsHealth PolicyHealth InformaticsComputer Science ApplicationsSDG 3 - Good Health and Well-beingOliveira, M., Seringa, J., Pinto, F. J., Henriques, R., & Magalhães, T. (2023). Machine learning prediction of mortality in Acute Myocardial Infarction. BMC Medical Informatics and Decision Making, 23(1), 1-16. [70]. https://doi.org/10.1186/s12911-023-02168-6. --- The present publication was funded by Fundação Ciência e Tecnologia, IP national support through CHRC (UIDP/04923/2020). The funding body did not played any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.Abstract Background Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients with AMI upon admission, using different variables to analyse their impact on predictive models. Methods Three experiments were built for mortality in AMI in a Portuguese hospital between 2013 and 2015 using various machine learning techniques. The three experiments differed in the number and type of variables used. We used a discharged patients’ episodes database, including administrative data, laboratory data, and cardiac and physiologic test results, whose primary diagnosis was AMI. Results Results show that for Experiment 1, Stochastic Gradient Descent was more suitable than the other classification models, with a classification accuracy of 80%, a recall of 77%, and a discriminatory capacity with an AUC of 79%. Adding new variables to the models increased AUC in Experiment 2 to 81% for the Support Vector Machine method. In Experiment 3, we obtained an AUC, in Stochastic Gradient Descent, of 88% and a recall of 80%. These results were obtained when applying feature selection and the SMOTE technique to overcome imbalanced data. Conclusions Our results show that the introduction of new variables, namely laboratory data, impacts the performance of the methods, reinforcing the premise that no single approach is adapted to all situations regarding AMI mortality prediction. Instead, they must be selected, considering the context and the information available. Integrating Artificial Intelligence (AI) and machine learning with clinical decision-making can transform care, making clinical practice more efficient, faster, personalised, and effective. AI emerges as an alternative to traditional models since it has the potential to explore large amounts of information automatically and systematically.Escola Nacional de Saúde Pública (ENSP)NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolComprehensive Health Research Centre (CHRC) - Pólo ENSPCentro de Investigação em Saúde Pública (CISP/PHRC)RUNOliveira, MarianaSeringa, JoanaPinto, Fausto JoséHenriques, RobertoMagalhães, Teresa2023-04-18T22:21:01Z2023-04-182023-04-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16application/pdfhttp://hdl.handle.net/10362/151915eng1472-6947PURE: 58591118https://doi.org/10.1186/s12911-023-02168-6info: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:RCAAP2024-03-11T05:34:16Zoai:run.unl.pt:10362/151915Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:43.481249Repositório 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 Machine learning prediction of mortality in Acute Myocardial Infarction
title Machine learning prediction of mortality in Acute Myocardial Infarction
spellingShingle Machine learning prediction of mortality in Acute Myocardial Infarction
Oliveira, Mariana
Machine learning
Cardiovascular diseases
Acute Myocardial Infarction
Predictive models
Health Policy
Health Informatics
Computer Science Applications
SDG 3 - Good Health and Well-being
title_short Machine learning prediction of mortality in Acute Myocardial Infarction
title_full Machine learning prediction of mortality in Acute Myocardial Infarction
title_fullStr Machine learning prediction of mortality in Acute Myocardial Infarction
title_full_unstemmed Machine learning prediction of mortality in Acute Myocardial Infarction
title_sort Machine learning prediction of mortality in Acute Myocardial Infarction
author Oliveira, Mariana
author_facet Oliveira, Mariana
Seringa, Joana
Pinto, Fausto José
Henriques, Roberto
Magalhães, Teresa
author_role author
author2 Seringa, Joana
Pinto, Fausto José
Henriques, Roberto
Magalhães, Teresa
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Escola Nacional de Saúde Pública (ENSP)
NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
Comprehensive Health Research Centre (CHRC) - Pólo ENSP
Centro de Investigação em Saúde Pública (CISP/PHRC)
RUN
dc.contributor.author.fl_str_mv Oliveira, Mariana
Seringa, Joana
Pinto, Fausto José
Henriques, Roberto
Magalhães, Teresa
dc.subject.por.fl_str_mv Machine learning
Cardiovascular diseases
Acute Myocardial Infarction
Predictive models
Health Policy
Health Informatics
Computer Science Applications
SDG 3 - Good Health and Well-being
topic Machine learning
Cardiovascular diseases
Acute Myocardial Infarction
Predictive models
Health Policy
Health Informatics
Computer Science Applications
SDG 3 - Good Health and Well-being
description Oliveira, M., Seringa, J., Pinto, F. J., Henriques, R., & Magalhães, T. (2023). Machine learning prediction of mortality in Acute Myocardial Infarction. BMC Medical Informatics and Decision Making, 23(1), 1-16. [70]. https://doi.org/10.1186/s12911-023-02168-6. --- The present publication was funded by Fundação Ciência e Tecnologia, IP national support through CHRC (UIDP/04923/2020). The funding body did not played any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
publishDate 2023
dc.date.none.fl_str_mv 2023-04-18T22:21:01Z
2023-04-18
2023-04-18T00:00:00Z
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/10362/151915
url http://hdl.handle.net/10362/151915
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1472-6947
PURE: 58591118
https://doi.org/10.1186/s12911-023-02168-6
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 16
application/pdf
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
institution RCAAP
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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