Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2602.17949v1</id>\n    <title>CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications</title>\n    <updated>2026-02-20T03:00:13Z</updated>\n    <link href='https://arxiv.org/abs/2602.17949v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2602.17949v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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 supertypes. Constructing such concept sets is labour-intensive, inconsistently performed, and poorly supported by existing tools, particularly for NLP pipelines that operate directly on UMLS CUIs. 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. For each target concept, candidate CUIs were retrieved from the KG, followed by large language model (LLM) filtering and classification steps comparing two LLMs (GPT-5 and GPT-5-mini). The framework was evaluated on five lexically heterogeneous clinical concepts against a manually curated benchmark and gold-standard concept sets. Results Across all concepts, CUICurate produced substantially larger and more complete concept sets than the manual benchmarks whilst matching human precision. Comparisons between the two LLMs found that GPT-5-mini achieved higher recall during filtering, while GPT-5 produced classifications that more closely aligned with clinician judgements. Outputs were stable across repeated runs and computationally inexpensive. Conclusions CUICurate offers a scalable and reproducible approach to support UMLS concept set curation that substantially reduces manual effort. By integrating graph-based retrieval with LLM reasoning, the framework produces focused candidate concept sets that can be adapted to clinical NLP pipelines for different phenotyping and analytic requirements.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n    <published>2026-02-20T03:00:13Z</published>\n    <arxiv:comment>30 pages, 6 figures, 4 tables</arxiv:comment>\n    <arxiv:primary_category term='cs.CL'/>\n    <author>\n      <name>Victoria Blake</name>\n    </author>\n    <author>\n      <name>Mathew Miller</name>\n    </author>\n    <author>\n      <name>Jamie Novak</name>\n    </author>\n    <author>\n      <name>Sze-yuan Ooi</name>\n    </author>\n    <author>\n      <name>Blanca Gallego</name>\n    </author>\n  </entry>"
}