Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs

Jose Geraldo Fernandes, Luiz Facury de Souza, Pedro Robles Dutenhefner, Wagner Meira Jr.

Keywords: World Models, Self-Supervised Learning, Representation Disentanglement

TL;DR: We propose an Action-Conditioned World Model for cardiac monitoring that treats disease as a transition vector in a JEPA latent space, achieving superior sample efficiency by disentangling anatomy from pathology.

Abstract: Self-supervised learning in healthcare has largely relied on invariance-based objectives, which maximize similarity between different views of the same patient. While effective for static anatomy, this paradigm is fundamentally misaligned with clinical diagnosis, as it mathematically compels the model to suppress the transient pathological changes it is intended to detect. We propose a shift towards Action-Conditioned World Models that learn to simulate the dynamics of disease progression, or \emph{Event-Conditioned}. Adapting the LeJEPA framework to physiological time-series, we define pathology not as a static label, but as a transition vector acting on a patient's latent state. By predicting the future electrophysiological state of the heart given a disease onset, our model explicitly disentangles stable anatomical features from dynamic pathological forces. Evaluated on the MIMIC-IV-ECG dataset, our approach outperforms fully supervised baselines on the critical triage task. Crucially, we demonstrate superior sample efficiency: in low-resource regimes, our world model outperforms supervised learning by over 0.05 AUROC. These results suggest that modeling biological dynamics provides a dense supervision signal that is far more robust than static classification.

Submission Number: 63