解锁电子健康记录：一种混合图RAG方法用于安全临床AI的患者问答
Unlocking Electronic Health Records: A Hybrid Graph RAG Approach to Safe Clinical AI for Patient QA
AI总结：
本文提出MediGRAF，一种结合图检索与向量嵌入的混合RAG系统，通过整合结构化数据与非结构化文本，提升临床AI在患者问答中的安全性和准确性。
摘要：
电子健康记录（EHR）系统为临床医生提供了庞大的临床信息库，但关键细节容易被忽视。尽管大语言模型（LLM）在数据处理方面具有变革潜力，但其在临床环境中面临显著限制，尤其是在上下文关联和幻觉方面。当前解决方案通常孤立地处理结构化数据（SQL/Cypher）或非结构化语义搜索，无法同时整合两者。本文提出了MediGRAF（医学图检索增强框架），一种新颖的混合图RAG系统，填补了这一空白。通过结合Neo4j Text2Cypher的能力进行结构化关系遍历与向量嵌入进行非结构化叙述检索，MediGRAF实现了对完整患者旅程的自然语言查询。使用MIMIC-IV数据集中的10名患者（生成5,973个节点和5,963个关系），我们生成了足够的节点和数据用于患者层面的问题解答（QA），并在不同查询复杂度下评估了该架构。系统在事实性查询中实现了100%的召回率，意味着所有相关信息都被检索并出现在输出中，而复杂推理任务的平均专家质量得分为4.25/5，且无安全违规。这些结果表明，混合图关联显著推动了临床信息检索，提供了一种更安全、更全面的替代标准LLM部署方案。
英文摘要：
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.
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。
