Key result
Ensemble machine learning improves STEMI in-hospital mortality prediction over TIMI score to an AUC of ~0.96.
Why the study?
Do ensemble machine learning models improve in-hospital mortality prediction compared to the TIMI risk score in Asian patients with STEMI?
Do ensemble machine learning models improve in-hospital mortality prediction compared to the TIMI risk score in Asian patients with STEMI?
Answers are grounded in this paper and cite their sources.
Ensemble machine learning models integrated with explainable AI and a chatbot significantly improve in-hospital mortality risk prediction for Asian STEMI patients compared to traditional TIMI risk scores.
Background ST-elevation myocardial infarction (STEMI) is a leading cause of mortality, with significant variations in risk profiles among the Asian population. Traditional risk scores, such as TIMI, are developed based on Western cohorts and have limited predictive accuracy for Asian patients. Machine learning (ML) models have shown superior performance but remain underutilized due to a lack of accessible and interpretable tools. Purpose This study develops a web-based application integrating ensemble learning, explainable AI (XAI), and a chatbot for STEMI mortality risk prediction. The system provides transparent assessments, enhancing clinical decision-making for Asian healthcare providers. Methods A dataset of 42,348 STEMI cases (2006–2019) from the NCVD registry with 54 clinical features was used. Data preprocessing included handling missing values, outlier detection, and feature normalization. Feature selection was performed using recursive feature elimination (RFE) and expert input. Ensemble models, including Random Forest, Gradient Boosting, XGBoost, and Stack Ensemble, were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) provided interpretability of model predictions, with a user-friendly interface displaying summary plots and individual risk factor explanations. A chatbot, powered by a fine-tuned large language model (LLM), was developed for real-time guidance. The chatbot was localized with region-specific clinical guidelines, terminology, and frequently asked questions to improve usability for Asian healthcare providers. The LangChain framework facilitated seamless knowledge retrieval, enhancing chatbot interactions. A prospective data collection feature enabled continuous model validation and refinement by incorporating new patient data over time. Results The ensemble models outperformed traditional risk scoring methods, achieving an AUC score of 0.96, Recall: 0.89, Precision:0.61for in-hospital mortality prediction, compared to the TIMI risk score (AUC 0.81). SHAP analysis identified key predictors, providing interpretable insights into risk stratification. The chatbot improved accessibility, offering real-time assistance for clinicians in risk assessment. The prospective data collection feature ensured ongoing model updates, maintaining predictive accuracy and clinical relevance. Conclusion This study presents a web-based STEMI risk prediction tool integrating ensemble learning, XAI, and a chatbot, tailored for the Asian population. The system enhances predictive accuracy, interpretability, and usability, bridging the gap between advanced ML models and clinical practice. By combining AI-driven risk assessment with a user-friendly interface, this tool provides a scalable solution for improving STEMI outcomes in Asian healthcare settings.
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Kasim et al. (2025) studied ST-elevation myocardial infarction (STEMI) (n=42,348). Ensemble machine learning models vs. TIMI risk score was evaluated on in-hospital mortality prediction. Ensemble machine learning models outperformed the traditional TIMI risk score for in-hospital mortality prediction in Asian STEMI patients (AUC 0.96 vs 0.81).
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