MedRAG：通过知识图谱引导的推理增强医疗Copilot的检索增强生成
MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot
AI总结：
本文提出MedRAG，一种由知识图谱引导推理增强的医疗RAG模型，通过构建四层分级诊断知识图谱并动态整合相似EHR，在大型语言模型中推理，提供更准确的诊断决策支持，有效降低误诊率。
摘要：
检索增强生成（RAG）是一种非常适合检索隐私敏感的电子健康记录（EHR）的技术。它可以作为医疗Copilot的关键模块，帮助减少医疗从业者和患者的误诊。然而，医疗领域现有的基于启发式的RAG模型在诊断准确性和特异性方面存在不足，特别是对于具有相似表现的疾病。本文提出了MedRAG，一种由知识图谱（KG）引导推理增强的面向医疗领域的RAG模型，该模型基于临床表现检索诊断和治疗建议。MedRAG系统地构建了一个全面的四层分级诊断知识图谱，涵盖了各种疾病的关键诊断差异。这些差异与从EHR数据库中检索到的相似EHR动态整合，并在大型语言模型中进行推理。这一过程能够提供更准确、更具体的决策支持，同时主动提供后续问题以增强个性化医疗决策制定。MedRAG在公开数据集DDXPlus和从Tan Tock Seng Hospital收集的私有慢性疼痛诊断数据集（CPDD）上进行了评估，并将其性能与各种现有的RAG方法进行了比较。实验结果表明，利用知识图谱的信息整合和关系能力，我们的MedRAG提供了更具体的诊断见解，并在降低误诊率方面优于最先进的模型。我们的代码将在 https://github.com/SNOWTEAM2023/MedRAG 上提供
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
