Publication
Machine learning prediction of mortality in acute myocardial infarction
| dc.contributor.author | Oliveira, Mariana | |
| dc.contributor.author | Seringa, Joana | |
| dc.contributor.author | Pinto, Fausto J. | |
| dc.contributor.author | Henriques, Roberto | |
| dc.contributor.author | Magalhães, Teresa | |
| dc.date.accessioned | 2023-04-27T14:09:32Z | |
| dc.date.available | 2023-04-27T14:09:32Z | |
| dc.date.issued | 2023 | |
| dc.description | © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | pt_PT |
| dc.description.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. | pt_PT |
| dc.description.sponsorship | The present publication was funded by Fundação Ciência e Tecnologia, IP national support through CHRC (UIDP/04923/2020). | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | BMC Med Inform Decis Mak. 2023 Apr 18;23(1):70 | pt_PT |
| dc.identifier.doi | 10.1186/s12911-023-02168-6 | pt_PT |
| dc.identifier.eissn | 1472-6947 | |
| dc.identifier.uri | http://hdl.handle.net/10451/57277 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer Nature | pt_PT |
| dc.relation | Comprehensive Health Research Center - Research, Education, Training and Innovation in Clinical research and Public Health | |
| dc.relation.publisherversion | https://bmcmedinformdecismak.biomedcentral.com/ | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Acute Myocardial Infarction | pt_PT |
| dc.subject | Cardiovascular diseases | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Predictive models | pt_PT |
| dc.title | Machine learning prediction of mortality in acute myocardial infarction | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Comprehensive Health Research Center - Research, Education, Training and Innovation in Clinical research and Public Health | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04923%2F2020/PT | |
| oaire.citation.issue | 1 | pt_PT |
| oaire.citation.title | BMC Medical Informatics and Decision Making | pt_PT |
| oaire.citation.volume | 23 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Pinto | |
| person.givenName | Fausto J. | |
| person.identifier | 1308889 | |
| person.identifier.ciencia-id | C311-AEDD-6DBB | |
| person.identifier.orcid | 0000-0002-8034-4529 | |
| person.identifier.rid | G-9363-2015 | |
| person.identifier.scopus-author-id | 7102740158 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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