[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118784-en":3,"doc-seo-118784-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118784,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine learning prediction of mortality in Acute Myocardial Infarction","Modeling acute myocardial infarction (AMI) mortality through machine learning supports predictive analysis at admission using multiple variable types. Using a discharged patients’ episodes database with administrative, laboratory, and cardiac/physiologic test results from a Portuguese hospital (2013–2015), three experiments compared different variable sets and machine learning techniques. Results report improved discrimination with additional variables, including enhanced AUC and recall, while feature selection and SMOTE addressed imbalanced data. Findings emphasize selecting context-appropriate models and integrating AI to improve clinical decision-making.","Oliveira et al.  \nBMC Medical Informatics and Decision Making [https://doi.org/10.1186/s12911-023-02168-6](https://doi.org/10.1186/s12911-023-02168-6)  \n(2023) 23:70  \nBMC Medical Informatics and Decision Making  \n RESEARCH Open Access  \nMachine learning prediction of mortality in Acute Myocardial Infarction  \nMariana Oliveira1, Joana Seringa1, Fausto José Pinto2, Roberto Henriques3* and Teresa Magalhães4  \nAbstract  \nBackground 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.  \nMethods 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.  \nResults 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.  \nConclusions 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.  \nKeywords Machine learning, Cardiovascular diseases, Acute Myocardial Infarction, Predictive models  \n*Correspondence: Roberto Henriques [roberto@novaims.unl.pt](roberto@novaims.unl.pt)  \n1 NOVA National School of Public Health, Universidade NOVA Lisboa, Lisbon, Portugal  \n2 Serviço de Cardiologia, Centro Hospitalar Universitário de Lisboa Norte (CHULN), CAML, CCUL, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal  \n3 NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal  \n4 NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, Nova University of Lisbon, Lisbon, Portugal  \nBackground  \nCardiovascular diseases are the leading cause of death in the European Union (EU) and the United States of America, representing approximately 30% of deaths [1–3] . In cardiovascular diseases, Acute Myocardial Infarction (AMI) is still the leading cause of death and hospitalisation in Portugal and globally [3], representing 3.3% of the total deaths in Portugal [4] . Moreover, the effects of COVID‐19 demonstrated the need to maintain access to high-quality acute care for AMI, as significant rises in AMI mortality rates were seen during this period [3, 5, 6]. In recent decades, introducing new technologies, optimising therapeutic means, and preventive  \n© 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 cred","cbCaijRlt7ujePIy","https://ap.wps.com/l/cbCaijRlt7ujePIy","pdf",1728027,1,16,"English","en",105,"# Abstract\n# Background\n# Methods\n# Results\n# Conclusions","[{\"question\":\"How was the machine learning model for AMI mortality prediction constructed?\",\"answer\":\"The study created three mortality prediction experiments using a discharged patients’ episodes database containing administrative data, laboratory data, and cardiac/physiologic test results. Experiments differed in the number and type of variables used.\"},{\"question\":\"Which techniques were used to address class imbalance and improve model performance?\",\"answer\":\"Feature selection was applied and the SMOTE technique was used to overcome imbalanced data, supporting improved AUC and recall across experiments.\"},{\"question\":\"What was the main finding about adding new variables to the models?\",\"answer\":\"Introducing new variables, particularly laboratory data, increased predictive performance. The authors conclude that no single approach fits all situations for AMI mortality prediction, so methods should be chosen based on available information and context.\"}]","Machine learning prediction of mortality in Acute Myocardial Infarction | PDF",1785720243,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-prediction-of-mortality-in-acute-myocardial-infarction","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-prediction-of-mortality-in-acute-myocardial-infarction/118784/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How was the machine learning model for AMI mortality prediction constructed?","Question",{"text":75,"@type":76},"The study created three mortality prediction experiments using a discharged patients’ episodes database containing administrative data, laboratory data, and cardiac/physiologic test results. Experiments differed in the number and type of variables used.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which techniques were used to address class imbalance and improve model performance?",{"text":80,"@type":76},"Feature selection was applied and the SMOTE technique was used to overcome imbalanced data, supporting improved AUC and recall across experiments.",{"name":82,"@type":73,"acceptedAnswer":83},"What was the main finding about adding new variables to the models?",{"text":84,"@type":76},"Introducing new variables, particularly laboratory data, increased predictive performance. The authors conclude that no single approach fits all situations for AMI mortality prediction, so methods should be chosen based on available information and context.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]