患者不変性を超えて：行動条件付きJEPAによる心臓ダイナミクスの学習
Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs
自己教師あり学習の不変性目的が病態変化を抑制する問題を指摘し、疾患進行を遷移ベクトルとして捉える行動条件付き世界モデルを提案。心電図データで教師あり学習を上回る性能とサンプル効率を実証した。
著者: Jose Geraldo Fernandes, Luiz Facury, Pedro Robles Dutenhefner, Wagner Meira
分類: cs.LG
原文アブストラクト
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 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. Source code is available at https://github.com/cljosegfer/lesaude-dynamics