Related Experiment Video
Updated: Sep 23, 2025
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Machine learning based model for risk prediction after ST-Elevation myocardial infarction: Insights from the North
Manu Kumar Shetty1, Shekhar Kunal2, M P Girish2
1Department of Clinical Pharmacology, Maulana Azad Medical College, Delhi, India.
Insights
Machine learning models improve mortality prediction for ST-Elevation Myocardial Infarction (STEMI) patients in resource-limited settings. Key predictors include delayed revascularization and heart failure, guiding intensive patient monitoring.
Area of Science:
- Cardiology
- Artificial Intelligence
- Health Informatics
Background:
- Accurate mortality risk prediction for ST-Elevation Myocardial Infarction (STEMI) is crucial in resource-limited countries.
- Identifying high-risk patients enables targeted, intensive management strategies.
Purpose of the Study:
- To develop and validate machine learning (ML) models for predicting 30-day mortality in STEMI patients.
- To identify key clinical predictors of mortality using interpretable AI.
Main Methods:
- Utilized data from the North India ST-Elevation Myocardial Infarction (NORIN-STEMI) registry (3191 patients).
- Trained and validated various ML models using 31 clinical characteristics.
- Employed Shapley Additive exPlanations (ShAP) for model interpretability.
Main Results:
- The Extra Tree ML model demonstrated strong predictive performance (AUC: 79.7%, Accuracy: 75%) on the validation set.
- Identified significant mortality predictors: delayed revascularization, cardiogenic shock, low ejection fraction, elevated creatinine, heart failure, female sex, and mitral regurgitation.
- Overall 30-day mortality in the study cohort was 7.7%.
Conclusions:
- ML models offer enhanced mortality prediction for STEMI patients.
- Interpretable AI (ShAP) effectively highlights critical risk factors, aiding in the identification of individuals requiring intensified monitoring and care.
Background:
Risk prediction following ST-Elevation Myocardial Infarction (STEMI) in resource limited countries is critical to identify patients at an increased risk of mortality who might benefit from intensive management.
Methods:
North India ST-Elevation Myocardial Infarction (NORIN-STEMI) is an ongoing registry that has prospectively enrolled 3,635 STEMI patients. Of these, 3191 patients with first STEMI were included. Patients were divided into two groups: development (n=2668) and validation (unseen) dataset (n=523). Various ML strategies were used to train and tune the model based on validation dataset results that included 31 clinical characteristics. These models were compared in sensitivity, specificity, F1-score, receiver operating characteristic area under the curve (AUC), and overall accuracy to predict mortality at 30 days. ML model decision making was analyzed using the Shapley Additive exPlanations (ShAP) summary plot.
Results:
At 30 days, the mortality was 7.7%. On the validation dataset, Extra Tree ML model had the best predictive ability with sensitivity: 85%, AUC: 79.7%, and Accuracy: 75%. ShAP interpretable summary plot determined delay in time to revascularization, baseline cardiogenic shock, left ventricular ejection fraction <30%, age, serum creatinine, heart failure on presentation, female sex, and moderate-severe mitral regurgitation to be major predictors of all-cause mortality at 30 days (P<0.001 for all).
Conclusion:
ML models lead to an improved mortality prediction following STEMI. ShAP summary plot for the interpretability of the AI model helps to understand the model's decision in identifying high-risk individuals who may benefit from intensified follow-up and close monitoring.
More Related Videos
18:11A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
Published on: December 28, 2012
07:25Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
Published on: September 22, 2020
