让电子病历大模型的决策过程可解释，提升临床可信度。
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models
- 用Transformer构建代理模型，解析大模型每一步的决策依据。
- 识别出对预测影响最大的病历片段，与临床知识高度吻合。
- 适合关注医疗AI可解释性的医生、研究者和监管人员。
电子病历基础模型（FEMRs）在大规模结构化患者数据上预训练，能将长期患者病史转化为适用于多种临床预测任务的通用表示。尽管效果显著，但其黑箱特性引发对偏见、可解释性和临床信任的担忧。为此，我们提出首个面向FEMRs的词元级可解释性方法。通过在两个预测任务中训练基于Transformer的代理模型，拟合FEMR的输入输出行为，同时保留时间动态。我们识别出最具影响力的词元，揭示FEMRs如何利用患者历史的不同方面进行预测。为评估临床相关性，引入一种新型临床对齐度量，量化代理模型的关键词元与已验证临床特征的一致性。结果表明，代理模型能准确逼近FEMR预测，且词元级解释与临床知识高度一致，为可解释且可信的临床AI提供实用框架。
原文摘要 · Abstract (English)
Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks. Despite their effectiveness, FEMRs remain black-box models, raising concerns about bias, interpretability, and clinical trust. To address this, we propose the first token-level explainability approach for FEMRs. We train a Transformer-based surrogate model on input-output pairs from the FEMR across two prediction tasks, approximating its behavior while preserving temporal dynamics. We identify the most influential tokens, providing insights into how FEMRs leverage different aspects of patient history for predictions. To evaluate clinical relevance, we introduce a novel clinical alignment metric that quantifies the correspondence between the surrogate model's key tokens and clinically validated features. Our results demonstrate that the surrogate closely approximates FEMR predictions and that token-level explanations align well with clinical knowledge, offering a practical framework for interpretable and trustworthy clinical AI.
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