用病历经验增强大模型，提升出院问答准确性
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA
- 基于电子病历检索相似患者经历，动态补充临床上下文
- 在1280个真实出院问题上平均提升5.2%准确率
- 适合需要结合真实病例推理的医疗AI研究者
为提升大语言模型在临床应用中的可靠性，检索增强生成（RAG）被广泛用于提供医学事实知识。然而，除来自开放数据集的一般医学知识外，基于临床案例的知识对有效医疗推理同样关键，因其提供了源于真实患者经历的上下文。受此启发，我们提出基于电子健康记录（EHR）的体验检索增强框架ExpRAG，旨在从其他患者的出院报告中提供相关背景。ExpRAG采用粗到精的检索流程，先通过基于EHR的报告排序器高效识别相似患者，再由经验检索器提取任务相关的具体内容以增强医疗推理。为评估ExpRAG，我们引入DischargeQA，一个包含1,280个与出院相关的问答任务的临床问答数据集，涵盖诊断、用药和指导等任务。每个问题均基于EHR数据生成，确保场景真实且具挑战性。实验结果表明，ExpRAG持续优于基于文本的排序器，平均相对提升5.2%，凸显了基于病例的知识对医疗推理的重要性。
原文摘要 · Abstract (English)
To improve the reliability of Large Language Models (LLMs) in clinical applications, retrieval-augmented generation (RAG) is extensively applied to provide factual medical knowledge. However, beyond general medical knowledge from open-ended datasets, clinical case-based knowledge is also critical for effective medical reasoning, as it provides context grounded in real-world patient experiences.Motivated by this, we propose Experience Retrieval-Augmentation ExpRAG framework based on Electronic Health Record(EHR), aiming to offer the relevant context from other patients' discharge reports. ExpRAG performs retrieval through a coarse-to-fine process, utilizing an EHR-based report ranker to efficiently identify similar patients, followed by an experience retriever to extract task-relevant content for enhanced medical reasoning.To evaluate ExpRAG, we introduce DischargeQA, a clinical QA dataset with 1,280 discharge-related questions across diagnosis, medication, and instruction tasks. Each problem is generated using EHR data to ensure realistic and challenging scenarios. Experimental results demonstrate that ExpRAG consistently outperforms a text-based ranker, achieving an average relative improvement of 5.2%, highlighting the importance of case-based knowledge for medical reasoning.
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