Dynamical Predictive Modelling of Cardiovascular Disease Progression Post-Myocardial Infarction via ECG-Trained Artificial Intelligence Model

TL;DR

This study introduces a pretrained AI model that leverages unlabelled ECG data and patient-specific information to improve post-myocardial infarction outcome prediction, outperforming models trained from scratch.

Contribution

The paper presents a novel combination of contrastive learning and supervised multitask training for ECG-based prognosis in cardiac disease, addressing data scarcity issues.

Findings

Pretrained model achieved higher AUC (0.794) compared to from-scratch model (0.608).

Clinically structured ECG modeling enhances classification accuracy in limited data settings.

Abstract

Myocardial infarction (MI) is a leading cause of death, and its adverse outcomes are urgent to predict. Yet ECG-based prognostic models underperform because deep learning requires large, labelled datasets, which are scarce in medicine. Foundation models can learn from unlabelled ECGs via selfsupervision, but medically relevant training strategies remain underexplored. We propose a pretrained artificial intelligence model that combines patient-specific temporal information using contrastive learning with supervised multitask heads, then fine-tunes on post-MI outcome prediction. The proposed model outperformed a model trained from scratch (0.794 vs 0.608 AUC) showing that clinically structured ECG modelling improves classification in limited data regimes.