用图谱增强大模型自动构建临床概念集，省时高效且准确率高。
CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications
- 基于图谱检索与大模型分类，自动扩展并筛选医学概念
- 对5个概念的召回率达95%以上，优于人工标注基准
- 适合医疗文本挖掘、表型识别等临床NLP任务使用
临床命名实体识别工具常将自由文本映射到统一医学语言系统（UMLS）的概念唯一标识符（CUI）。然而，下游任务中真正有意义的单位往往是包含相关同义词、亚型和关联概念的概念集合。构建这些集合耗时费力、标准不一，现有工具支持有限。本文提出CUICurate，一种基于图谱检索增强生成（GraphRAG）的自动化UMLS概念集构建框架。构建了嵌入的UMLS知识图谱用于语义检索，通过图扩展获取候选CUI，再由GPT-5和Qwen3-32B大模型进行过滤与分类。在五个词汇异质性高的临床概念上评估，对比人工构建和金标准概念集。结果表明，CUICurate生成的概念集更大更完整；单一检索配置即能实现高召回率，候选集规模可控。GPT-5在所有概念上表现优于人工，保留至少95%的金标准确信度CUI，Qwen3-32B表现相近但略低。许多未被捕捉的概念在10,000条MIMIC-III病历中也未出现。该框架基础设施和端到端流程成本低、运行稳定。
原文摘要 · Abstract (English)
Background: Clinical named entity recognition tools commonly map free text to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). For many downstream tasks, however, the clinically meaningful unit is not a single CUI but a concept set comprising related synonyms, subtypes, and associated concepts. Constructing these sets is labour-intensive, inconsistently performed, and poorly supported by existing tools. Methods We present CUICurate, a graph-based retrieval-augmented generation (GraphRAG) framework for automated UMLS concept set curation. A UMLS knowledge graph (KG) was constructed and embedded for semantic retrieval. Candidate CUIs were retrieved using graph-based expansion and then filtered and classified using large language models (GPT-5 and Qwen3-32B). The framework was evaluated on five lexically heterogeneous clinical concepts against a manually curated concept sets and gold-standard concept sets. Results CUICurate produced substantially larger and more complete concept sets than the manual benchmarks. A single retrieval configuration across concepts achieved high recall of definitive concepts with manageable candidate sets. GPT-5 outperformed manual curation for all concepts and retained at least 95% of definitive gold-standard CUIs, while Qwen3-32B achieved comparable but slightly lower performance. Many missed concepts were not observed in 10,000 MIMIC-III notes. CUICurate infrastructure and end-to-end processing was inexpensive and stable across runs. Conclusions CUICurate offers a scalable, reproducible and cost-efficient approach for generating clinician-reviewable UMLS concept sets tailored to clinical natural language processing and phenotyping applications.
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