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September 18, 2025Canadian Journal of CardiologyOpen Access

Explainable Artificial Intelligence-Driven Risk Assessment for Malignant Ventricular Arrhythmia and Mortality in Acute Myocardial Infarction

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Authors

DCDabei CaiTSTingting SunJWJun Wei

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Overview

This analysis shows the predictive performance of AI models for malignant ventricular arrhythmia and mortality in acute myocardial infarction, indicating enhanced risk assessment capabilities.

Key Points

  • The XGBoost model achieved an AUROC of 0.792 for predicting composite endpoints, highlighting its effectiveness.
  • Among the models tested, LightGBM demonstrated the highest predictive performance for malignant ventricular arrhythmia with an AUROC of 0.827.
  • Random Forest was superior for mortality prediction with an AUROC of 0.784, providing critical insights into mortality risk.
  • The web-based AI system offers real-time risk assessment and personalized risk management for acute myocardial infarction patients.

Cite This Study

Cai et al. (2025) studied this question.

synapsesocial.com/papers/68d433b0713b0b5dfea734a6https://doi.org/10.1016/j.cjca.2025.09.015
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