用混合图检索增强框架，让AI安全准确回答患者病历问题
Unlocking Electronic Health Records: A Hybrid Graph RAG Approach to Safe Clinical AI for Patient QA
- 结合结构化查询与语义搜索，统一处理病历中的关系与文本
- 事实类问题召回率达100%，复杂推理得分4.25/5且无安全漏洞
- 适合医疗AI研发者和临床决策支持系统开发者使用
电子健康记录（EHR）系统为临床医生提供海量临床信息，但导致认知负担，关键信息易被遗漏。尽管大语言模型（LLM）在数据处理上潜力巨大，但在临床场景中面临上下文依赖和幻觉问题。现有方案通常只针对结构化数据（如SQL/Cypher）或非结构化语义搜索，无法同时整合两者。本文提出MediGRAF（Medical Graph Retrieval Augmented Framework），一种新型混合图检索增强框架，首次融合Neo4j Text2Cypher能力进行结构化关系遍历，与向量嵌入实现非结构化叙述性内容检索。基于MIMIC-IV数据集的10名患者（生成5,973个节点和5,963条关系），构建了可用于患者级问答的完整病程数据。在不同复杂度查询下评估该架构，结果显示：事实类查询召回率为100%（所有相关信息均被检索并输出），复杂推理任务平均专家评分达4.25/5，且零安全违规。结果表明，混合图引导显著提升了临床信息检索的安全性与全面性，为标准LLM部署提供了更优替代方案。
原文摘要 · Abstract (English)
Electronic health record (EHR) systems present clinicians with vast repositories of clinical information, creating a significant cognitive burden where critical details are easily overlooked. While Large Language Models (LLMs) offer transformative potential for data processing, they face significant limitations in clinical settings, particularly regarding context grounding and hallucinations. Current solutions typically isolate retrieval methods focusing either on structured data (SQL/Cypher) or unstructured semantic search but fail to integrate both simultaneously. This work presents MediGRAF (Medical Graph Retrieval Augmented Framework), a novel hybrid Graph RAG system that bridges this gap. By uniquely combining Neo4j Text2Cypher capabilities for structured relationship traversal with vector embeddings for unstructured narrative retrieval, MediGRAF enables natural language querying of the complete patient journey. Using 10 patients from the MIMIC-IV dataset (generating 5,973 nodes and 5,963 relationships), we generated enough nodes and data for patient level question answering (QA), and we evaluated this architecture across varying query complexities. The system demonstrated 100\% recall for factual queries which means all relevant information was retrieved and in the output, while complex inference tasks achieved a mean expert quality score of 4.25/5 with zero safety violations. These results demonstrate that hybrid graph-grounding significantly advances clinical information retrieval, offering a safer, more comprehensive alternative to standard LLM deployments.
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