让大模型读懂长期病历，提升临床预测准确率
EHR-RAG: Bridging Long-Horizon Structured Electronic Health Records and Large Language Models via Enhanced Retrieval-Augmented Generation
- 按事件和时间设计检索机制，保留病历的时间动态
- 迭代优化查询，覆盖更全面的临床证据
- 同时处理事实与反事实信息，适合医疗决策场景
电子健康记录（EHR）包含丰富的纵向临床数据，对医疗决策至关重要。现有基于大语言模型（LLM）的检索增强生成（RAG）方法在处理长期病历时，常因超出上下文长度限制而采用截断或简单检索，导致丢失关键临床事件和时间关联。为此，本文提出EHR-RAG框架，专为长周期结构化病历设计，包含三项创新：事件与时间感知的混合检索，保持临床结构与时间动态；自适应迭代检索，逐步优化查询以扩大证据覆盖范围；双路径证据检索与推理，联合获取并分析事实与反事实证据。在四个长期病历预测任务上，EHR-RAG显著优于最强基线，平均宏F1提升10.76%。结果表明，检索增强的LLM在真实临床预测中具有巨大潜力。
原文摘要 · Abstract (English)
Electronic Health Records (EHRs) provide rich longitudinal clinical evidence that is central to medical decision-making, motivating the use of retrieval-augmented generation (RAG) to ground large language model (LLM) predictions. However, long-horizon EHRs often exceed LLM context limits, and existing approaches commonly rely on truncation or vanilla retrieval strategies that discard clinically relevant events and temporal dependencies. To address these challenges, we propose EHR-RAG, a retrieval-augmented framework designed for accurate interpretation of long-horizon structured EHR data. EHR-RAG introduces three components tailored to longitudinal clinical prediction tasks: Event- and Time-Aware Hybrid EHR Retrieval to preserve clinical structure and temporal dynamics, Adaptive Iterative Retrieval to progressively refine queries in order to expand broad evidence coverage, and Dual-Path Evidence Retrieval and Reasoning to jointly retrieves and reasons over both factual and counterfactual evidence. Experiments across four long-horizon EHR prediction tasks show that EHR-RAG consistently outperforms the strongest LLM-based baselines, achieving an average Macro-F1 improvement of 10.76%. Overall, our work highlights the potential of retrieval-augmented LLMs to advance clinical prediction on structured EHR data in practice.
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