这篇论文用Transformer代理模型给医疗AI做局部解释，还能直接对齐临床知识，让黑箱诊断更可信。
电子健康记录基础模型（FEMR）预训练于大规模结构化患者数据，但缺乏可解释性。X-FEMR训练一个Transformer代理模型来近似FEMR在两个预测任务上的行为。该方法识别最影响预测的token，揭示患者历史不同方面的重要性。引入临床对齐度量，量化代理模型关键token与临床验证特征的一致性。实验表明代理模型能很好近似FEMR预测，token级解释与临床知识对齐。
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models
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.