将电子病历与大模型对齐，实现可解释的临床推理
ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
- 用任务感知重采样器对齐病历表示与大模型语义空间
- 在三个预测任务上同时提升推理质量与预测性能
- 适合需要可解释性医疗决策的临床研究者
大型语言模型（LLMs）在临床决策支持中具备强大的自然语言推理能力，但难以有效建模结构化的纵向电子病历（EHR）。相反，EHR基础模型能学习患者预测表示，却缺乏可解释的语言推理能力。为弥合这一差距，我们提出ChatHealthAI，一种多模态推理框架，通过任务感知重采样器将预训练EHR基础模型的结构化病历表示与冻结的大型语言模型（LLM）的语义空间对齐。通过整合纵向患者表示与精炼的临床事件描述，ChatHealthAI实现了基于临床事实的自然语言推理，同时保持了准确的患者预测能力。我们在EHRSHOT基准的三个临床预测任务上评估了ChatHealthAI。结果表明，该方法在提升推理质量与可解释性的同时，维持了具有竞争力的预测性能。这些发现凸显了将EHR基础模型与预训练语言模型结合，在可解释临床预测中的潜力。
原文摘要 · Abstract (English)
Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs). In contrast, EHR foundation models can learn predictive patient representations, yet lack interpretable language-based reasoning. To bridge this gap, we propose ChatHealthAI, a multimodal reasoning framework that aligns structured EHR representations from a pretrained EHR foundation model with the semantic space of a frozen LLM through a task-aware resampler. By integrating longitudinal patient representations with refined clinical event descriptions, ChatHealthAI enables clinically grounded natural-language reasoning while maintaining accurate patient prediction. We evaluated ChatHealthAI on three clinical predictive tasks from the EHRSHOT benchmark. Results show that ChatHealthAI improves reasoning quality and interpretability while preserving competitive predictive performance. These findings highlight the potential of integrating EHR foundation models with pretrained LLMs for interpretable clinical prediction.
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