ArticleBMC medical informatics and decision making2023
Machine learning prediction of mortality in Acute Myocardial Infarction.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk Measurement Techniques and Assessment of Their Effectiveness (Review).Sovremennye tekhnologii v meditsine · 2024Pooled it
- Article
- Limitations in Chest X-Ray Interpretation by Vision-Capable Large Language Models, Gemini 1.0, Gemini 1.5 Pro, GPT-4 Turbo, and GPT-4o.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence Tools in Myocardial Infarction Prognosis: Evaluating the Performance of Machine Learning and Deep Learning Models.Current cardiology reviews · 2026Review
- Comparing Machine Learning Models for Predicting Mortality after Myocardial Infarction: A Systematic Review and Meta-analysis.Archives of academic emergency medicine · 2026Review
- Comparison of the Prognostic Performance of Various Machine Learning Models in Patients with Acute Myocardial Infarction: Results from the COREA-AMI Registry.Medicina (Kaunas, Lithuania) · 2025Article
- The efficacy of artificial intelligence in predicting mortality rate and cardiogenic shock in acute coronary syndrome patients.International journal of cardiology. Cardiovascular risk and prevention · 2025Review
- Article
- ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning.Proceedings of machine learning research · 2025Article
- A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models.BMC medical informatics and decision making · 2025Observational
- Combined Risk Stratification With Patient Characteristics and Biomarkers in Patients Treated With the Impella for Cardiogenic Shock.Journal of the American Heart Association · 2025Article
- Profiling of Cardiogenic Shock: Incorporating Machine Learning Into Bedside Management.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Development and validation of machine learning models based on stacked generalization to predict psychosocial maladjustment in patients with acute myocardial infarction.BMC psychiatry · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Enhancing one-year mortality prediction in STEMI patients post-PCI: an interpretable machine learning model with risk stratification.Frontiers in artificial intelligence · 2025Article
- Diagnostic potential of increased Klotho and FGF23 protein concentrations after myocardial infarction in patients with acute coronary syndrome.Cardiology journal · 2025Article
- Model for Predicting the Effect of Sibutramine Therapy in Obesity.Journal of personalized medicine · 2024Article
- Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Improving the performance and interpretability on medical datasets using graphical ensemble feature selection.Bioinformatics (Oxford, England) · 2024Article
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute 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.
methodsThree 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.
resultsResults 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.
conclusionsOur 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.