ArticleScientific reports2021
Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis.European journal of medical research · 2023Pooled it
- Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review.Current cardiology reviews · 2023Pooled it
- Machine Learning-Based Risk Prediction of In-Hospital Mortality in Patients With Non-ST-Elevation Acute Coronary Syndrome at Various Stages of the Diagnostic Process: Observational Study.JMIR medical informatics · 2026Observational
- A machine learning model for early risk stratification of 28-day mortality after myocardial infarction.European heart journal. Digital health · 2026Article
- Comparing Machine Learning Models for Predicting Mortality after Myocardial Infarction: A Systematic Review and Meta-analysis.Archives of academic emergency medicine · 2026Review
- STEMI-OP in-hospital mortality prediction algorithms: Frailty-integrated machine learning in older patients undergoing primary PCI.npj aging · 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
- Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis.BMJ health & care informatics · 2025Article
- Predicting In-Hospital Mortality in Patients With Acute Myocardial Infarction: A Comparison of Machine Learning Approaches.Clinical cardiology · 2025Article
- Artificial Intelligence and Its Role in the Diagnosis and Prediction of Adverse Events in Acute Coronary Syndrome: A Narrative Review of the Literature.Life (Basel, Switzerland) · 2025Review
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models.BMC psychiatry · 2024Article
- A Machine Learning Model for Predicting In-Hospital Mortality in Chinese Patients With ST-Segment Elevation Myocardial Infarction: Findings From the China Myocardial Infarction Registry.Journal of medical Internet research · 2024Article
- Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention.Scientific reports · 2024Article
- 1-Year Mortality Prediction through Artificial Intelligence Using Hemodynamic Trace Analysis among Patients with ST Elevation Myocardial Infarction.Medicina (Kaunas, Lithuania) · 2024Article
- Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model.PloS one · 2024Article
- Article
- Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction.Cardiovascular digital health journal · 2023Article
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8 authors.
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Abstract
Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of ML models versus traditional models (TMs). This study developed ML-based models (logistic regression with regularization, random forest, support vector machine, and extreme gradient boosting) and compared their performance in predicting the short- and long-term mortality of patients with AMI with those of TMs with comparable predictors. The endpoints were the in-hospital mortality of 14,183 participants and the three- and 12-month mortality in patients who survived at discharge. The performance of the ML models in predicting the mortality of patients with an ST-segment elevation myocardial infarction (STEMI) was comparable to the TMs. In contrast, the areas under the curves (AUC) of the ML models for non-STEMI (NSTEMI) in predicting the in-hospital, 3-month, and 12-month mortality were 0.889, 0.849, and 0.860, respectively, which were superior to the TMs, which had corresponding AUCs of 0.873, 0.795, and 0.808. Overall, the performance of the predictive model could be improved, particularly for long-term mortality in NSTEMI, from the ML algorithm rather than using more clinical predictors.
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