用智能代理RAG精准提取病历信息，临床验证准确率达96.5%
Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why
- 构建基于智能体的RAG系统，综合处理海量异构病历数据
- 7326次临床验证中，96.5%提取结果被医生接受，单类准确率80%-99%
- 适合需要高可信医疗信息提取的临床研究与电子病历系统
患者病历涵盖数百份异构文档和数千个结构化数据点，但人工智能系统所需的文档级元数据常缺失或不完整。标准检索增强生成方法在此类数据上表现不佳，难以处理时间推理、跨文档依赖及缺失元数据问题。我们在埃森大学医学院部署了ACIE（智能体临床信息提取）系统：一个本地化的智能体RAG流程，能够对完整患者上下文进行推理，并将每个答案锚定在原始文本片段上以供医生验证。我们量化了元数据缺口，追溯其对架构设计的影响，并结合独立回顾性淋巴瘤注册研究进行评估，由核医学专家逐项比对提取值与其引用来源。在7,326次判断中，医生接受了96.5%的提取结果，各类别接受率在80%至99%之间。
原文摘要 · Abstract (English)
Patient contexts span hundreds of heterogeneous documents and thousands of structured data points, yet the document-level metadata that AI systems need for retrieval and triage is absent or incomplete. Standard retrieval-augmented generation fails on this data, mishandling temporal reasoning, cross-document dependencies, and missing metadata. We deploy ACIE (Agentic Clinical Information Extraction) at University Medicine Essen: an on-premise agentic RAG pipeline that reasons over complete patient contexts and grounds every answer in source passages for clinician verification. We quantify the metadata gap, trace the architectural decisions it shaped, and evaluate extraction alongside an independent retrospective lymphoma registry study, in which nuclear-medicine physicians verify every extracted value against its cited sources. Across 7,326 judgments, clinicians accepted 96.5\% of extractions, with per-type acceptance ranging from 80\% to 99\%.
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