用知识图谱提升医疗问答准确率，减少误诊。
MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot
- 构建四层疾病诊断知识图谱，挖掘相似病症的关键差异。
- 在慢性疼痛和公开数据集上，误诊率显著低于现有模型。
- 适合临床辅助决策系统开发人员及医疗AI研究者。
检索增强生成（RAG）是处理隐私敏感电子健康记录（EHR）的合适技术，可作为医疗助手的核心模块，帮助降低医患误诊率。然而，现有基于启发式规则的医疗RAG模型在表现相似疾病的诊断上准确性和特异性不足。本文提出MedRAG，一种通过知识图谱（KG）引导推理增强的医疗RAG模型，基于症状检索诊断与治疗建议。MedRAG系统构建涵盖多种疾病关键差异的四层分层诊断知识图谱，将这些差异动态融合于从EHR数据库中检索到的相似病例，并在大语言模型中进行推理。该过程提升了决策支持的准确性与特异性，同时主动生成后续追问以增强个性化医疗决策。MedRAG在公开数据集DDXPlus和新加坡淡滨尼综合医院收集的私有慢性疼痛诊断数据集CPDD上进行评估，与多种现有RAG方法对比。实验结果表明，借助知识图谱的信息整合与关系推理能力，MedRAG提供了更具体的诊断洞察，显著降低了误诊率，优于当前最优模型。代码将在https://github.com/SNOWTEAM2023/MedRAG发布。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) is a well-suited technique for retrieving privacy-sensitive Electronic Health Records (EHR). It can serve as a key module of the healthcare copilot, helping reduce misdiagnosis for healthcare practitioners and patients. However, the diagnostic accuracy and specificity of existing heuristic-based RAG models used in the medical domain are inadequate, particularly for diseases with similar manifestations. This paper proposes MedRAG, a RAG model enhanced by knowledge graph (KG)-elicited reasoning for the medical domain that retrieves diagnosis and treatment recommendations based on manifestations. MedRAG systematically constructs a comprehensive four-tier hierarchical diagnostic KG encompassing critical diagnostic differences of various diseases. These differences are dynamically integrated with similar EHRs retrieved from an EHR database, and reasoned within a large language model. This process enables more accurate and specific decision support, while also proactively providing follow-up questions to enhance personalized medical decision-making. MedRAG is evaluated on both a public dataset DDXPlus and a private chronic pain diagnostic dataset (CPDD) collected from Tan Tock Seng Hospital, and its performance is compared against various existing RAG methods. Experimental results show that, leveraging the information integration and relational abilities of the KG, our MedRAG provides more specific diagnostic insights and outperforms state-of-the-art models in reducing misdiagnosis rates. Our code will be available at https://github.com/SNOWTEAM2023/MedRAG
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