用心电图训练AI，精准预测心梗后病情发展。
Dynamical Predictive Modelling of Cardiovascular Disease Progression Post-Myocardial Infarction via ECG-Trained Artificial Intelligence Model
- 结合对比学习与多任务监督，融合患者时序信息建模。
- 在有限数据下AUC达0.794，显著优于从零训练模型。
- 适合心梗后风险预测，尤其数据稀缺的临床场景。
心肌梗死（MI）是主要致死原因，其不良预后亟需预测。然而，现有基于心电图（ECG）的预后模型性能不佳，因深度学习需大量标注数据，而医学数据标注稀缺。基础模型可通过自监督学习从无标签心电图中学习，但医学相关训练策略仍不明确。本文提出一种预训练人工智能模型，利用对比学习融合患者特异性时序信息，并采用多任务监督头进行微调，用于心梗后结局预测。该模型在独立测试集上表现优异，AUC达0.794，显著高于从零训练模型的0.608，证明临床结构化心电图建模在数据有限情况下能有效提升分类性能。
原文摘要 · Abstract (English)
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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