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arXiv · 2605.13568

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

Riccardo Cavarra·Lupo Lovatelli·Shaheim Ogbomo-Harmitt·Shahid Aziz·Adelaide De Vecchi·Andrew King·Oleg Aslanidi
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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.

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Riccardo Cavarra, Lupo Lovatelli, Shaheim Ogbomo-Harmitt, Shahid Aziz, Adelaide De Vecchi, Andrew King, Oleg Aslanidi. 2026-05-13. Dynamical Predictive Modelling of Cardiovascular Disease Progression Post-Myocardial Infarction via ECG-Trained Artificial Intelligence Model. https://doi.org/10.13140/rg.2.2.29492.21124

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