用多大模型从病历文本构建可验证的肿瘤知识图谱
Clinical Knowledge Graph Construction and Evaluation with Multi-LLMs via Retrieval-Augmented Generation
- 通过检索增强生成和多代理提示，直接从自由文本提取实体关系
- 在胰腺癌和乳腺癌数据上实现更高精度与本体一致性
- 支持自监督迭代优化，适合临床研究与医疗AI开发
大语言模型为从非结构化临床文本中构建知识图谱提供了新机遇。然而，现有方法常依赖结构化输入，且缺乏对事实准确性和语义一致性的可靠验证，这在肿瘤学领域尤为突出。我们提出一种端到端框架，直接从自由文本进行临床知识图谱的构建与评估，采用多代理提示与模式约束的检索增强生成（KG-RAG）策略。该流程整合了：(1) 提示驱动的实体、属性与关系抽取；(2) 基于熵的不确定性评分；(3) 与本体对齐的RDF/OWL模式生成；(4) 多大模型共识验证以检测幻觉并优化语义。除静态图谱构建外，该框架支持持续精炼与自监督评估，实现图谱质量的迭代提升。应用于两个肿瘤队列（PDAC 和 BRCA），方法生成了可解释、兼容 SPARQL 且基于临床的图谱，无需依赖标注金标准。实验表明，在精确度、相关性与本体合规性方面均优于基线方法。
原文摘要 · Abstract (English)
Large language models (LLMs) offer new opportunities for constructing knowledge graphs (KGs) from unstructured clinical narratives. However, existing approaches often rely on structured inputs and lack robust validation of factual accuracy and semantic consistency, limitations that are especially problematic in oncology. We introduce an end-to-end framework for clinical KG construction and evaluation directly from free text using multi-agent prompting and a schema-constrained Retrieval-Augmented Generation (KG-RAG) strategy. Our pipeline integrates (1) prompt-driven entity, attribute, and relation extraction; (2) entropy-based uncertainty scoring; (3) ontology-aligned RDF/OWL schema generation; and (4) multi-LLM consensus validation for hallucination detection and semantic refinement. Beyond static graph construction, the framework supports continuous refinement and self-supervised evaluation, enabling iterative improvement of graph quality. Applied to two oncology cohorts (PDAC and BRCA), our method produces interpretable, SPARQL-compatible, and clinically grounded knowledge graphs without relying on gold-standard annotations. Experimental results demonstrate consistent gains in precision, relevance, and ontology compliance over baseline methods.
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