ArticleCardiovascular digital health journal2023
Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction.
Article in Cardiovascular digital health journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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22 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
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- Enhancing large language model clinical support information with machine learning risk and explainability: a feasibility study.Intensive care medicine experimental · 2026Article
- Interpretable machine learning for predicting in-hospital bleeding after PCI in patients with diabetes and acute coronary syndrome: a retrospective cohort study.BMC cardiovascular disorders · 2026Article
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- Study on the Current Status of Supportive Care Needs of Elderly Breast Cancer Patients and Influencing Factors.Nursing research and practice · 2026Article
- Comparing Machine Learning Models for Predicting Mortality after Myocardial Infarction: A Systematic Review and Meta-analysis.Archives of academic emergency medicine · 2026Review
- AI-driven identification of nutrition-modulated biomarkers and drug targets for cardiovascular therapeutic mechanisms.Frontiers in pharmacology · 2026Review
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- A Novel Knowledge Fusion Ensemble for Diagnostic Differentiation of Pediatric Pneumonia and Acute Bronchitis.Diagnostics (Basel, Switzerland) · 2025Article
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6 authors.
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Abstract
Background: A lack of explainability in published machine learning (ML) models limits clinicians' understanding of how predictions are made, in turn undermining uptake of the models into clinical practice. Objective: The purpose of this study was to develop explainable ML models to predict in-hospital mortality in patients hospitalized for myocardial infarction (MI). Methods: Adult patients hospitalized for an MI were identified in the National Inpatient Sample between January 1, 2012, and September 30, 2015. The resulting cohort comprised 457,096 patients described by 64 predictor variables relating to demographic/comorbidity characteristics and in-hospital complications. The gradient boosting algorithm eXtreme Gradient Boosting (XGBoost) was used to develop explainable models for in-hospital mortality prediction in the overall cohort and patient subgroups based on MI type and/or sex. Results: The resulting models exhibited an area under the receiver operating characteristic curve (AUC) ranging from 0.876 to 0.942, specificity 82% to 87%, and sensitivity 75% to 87%. All models exhibited high negative predictive value ≥0.974. The SHapley Additive exPlanation (SHAP) framework was applied to explain the models. The top predictor variables of increasing and decreasing mortality were age and undergoing percutaneous coronary intervention, respectively. Other notable findings included a decreased mortality risk associated with certain patient subpopulations with hyperlipidemia and a comparatively greater risk of death among women below age 55 years. Conclusion: The literature lacks explainable ML models predicting in-hospital mortality after an MI. In a national registry, explainable ML models performed best in ruling out in-hospital death post-MI, and their explanation illustrated their potential for guiding hypothesis generation and future study design.
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