Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction.
Cardiovascular digital health journal(2023)
摘要
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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关键词
Acute coronary syndrome,Explainable machine learning,Myocardial infarction,In-hospital mortality, SHAP
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