Why the study?
Do explainable machine learning models improve in-hospital mortality prediction compared to the TIMI risk score in Asian STEMI patients?
Population
49,574 Asian STEMI patients in the Malaysian National Cardiovascular Disease registry (2006–2021)
Comparison
Explainable, well-calibrated machine learning… vs TIMI risk score
Design
Cohort
Follow-up
in-hospital
Authors
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Calibrated ML models outperform TIMI for Asian STEMI in-hospital mortality prediction; extends explainable, calibrated tools for risk stratification in underrepresented populations.
Do explainable machine learning models improve in-hospital mortality prediction compared to the TIMI risk score in Asian STEMI patients?
A calibrated logistic regression model with SHAP-based explainability significantly outperformed the traditional TIMI score for predicting in-hospital mortality in Asian STEMI patients.
Kasim et al. (2026) studied this question.