EHR-RAG: 通过增强检索增强生成连接长期结构电子健康记录和大型语言模型
EHR-RAG: Bridging Long-Horizon Structured Electronic Health Records and Large Language Models via Enhanced Retrieval-Augmented Generation
- University of Illinois Urbana-Champaign, United States（伊利诺伊大学厄巴纳-香槟分校）
- Yale University, United States（耶鲁大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
EHR-RAG通过增强检索增强生成方法，提升长期结构电子健康记录在临床预测中的表现，实现宏F1平均提升10.76%。
AI中文摘要：
电子健康记录（EHRs）提供了丰富的纵向临床证据，是医疗决策的核心，推动了检索增强生成（RAG）用于使大型语言模型（LLM）预测更加可靠。然而，长期EHRs往往超出LLM上下文限制，现有方法通常依赖于截断或普通检索策略，这些策略会丢弃临床相关事件和时间依赖性。为了解决这些挑战，我们提出了EHR-RAG，一个用于准确解释长期结构EHR数据的检索增强框架。EHR-RAG引入了三个组件，专门针对纵向临床预测任务：事件和时间感知混合EHR检索以保留临床结构和时间动态，自适应迭代检索以逐步细化查询以扩展广泛证据覆盖，以及双路径证据检索和推理以联合检索和推理事实和反事实证据。在四个长期EHR预测任务上的实验表明，EHR-RAG在所有任务上均优于最强的LLM基线，平均宏F1提升达10.76%。总体而言，我们的工作强调了检索增强LLM在实际中推动结构EHR数据临床预测的潜力。
英文摘要：
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.
