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.
@article{arxiv.2605.13568,
title = {Dynamical Predictive Modelling of Cardiovascular Disease Progression Post-Myocardial Infarction via ECG-Trained Artificial Intelligence Model},
author = {Riccardo Cavarra and Lupo Lovatelli and Shaheim Ogbomo-Harmitt and Shahid Aziz and Adelaide De Vecchi and Andrew King and Oleg Aslanidi},
journal= {arXiv preprint arXiv:2605.13568},
year = {2026}
}
Comments
submitted to the 9th International Conference on Computational and Mathematical Biomedical Engineering, 4 pages, 1 figure, 1 table