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  3. Cui-curate: A Graphrag-based Framework For Automated Clinical Concept Curation For Nlp Applications.
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  3. Cui-curate: A Graphrag-based Framework For Automated Clinical Concept Curation For Nlp Applications.

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Published on: June 13, 2025

CUI-Curate: a GraphRAG-based framework for automated clinical concept curation for NLP applications.

Victoria Blake1,2, Jamie Novak3, Mathew Miller4,5,6

  • 1Centre for Big Data Research in Health, University of New South Wales, Randwick, NSW 2031, Australia.

Journal of the American Medical Informatics Association : JAMIA
|June 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

CUI-Curate automates the creation of Unified Medical Language System (UMLS) concept sets using a graph-based approach. This tool generates larger, more complete sets than manual methods, improving clinical NLP and phenotyping.

Keywords:
Unified Medical Language Systemclinical natural language processingknowledge baseslarge language modelsphenotype

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Knowledge Representation

Background:

  • Clinical named entity recognition tools map text to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs).
  • Downstream tasks often require concept sets, not single CUIs, which are labor-intensive to create.
  • Existing tools poorly support the construction of comprehensive UMLS concept sets.

Purpose of the Study:

  • To present CUI-Curate, a novel graph-based retrieval-augmented-generation (GraphRAG) framework for automated UMLS concept set curation.
  • To enable scalable, reproducible, and cost-efficient generation of clinician-reviewable concept sets.
  • To enhance clinical natural language processing (NLP) and phenotyping applications.

Main Methods:

  • Constructed and embedded a UMLS knowledge graph for semantic retrieval.
  • Utilized graph-based expansion to retrieve candidate CUIs.
  • Filtered and classified candidate CUIs using large language models (GPT-5 and Qwen3-32B).

    • Main Results:

    • CUI-Curate generated substantially larger and more complete concept sets compared to manual benchmarks.
    • GPT-5 and Qwen3-32B models demonstrated high performance in classifying CUIs, with GPT-5 outperforming manual curation.
    • The framework achieved high recall of definitive concepts with manageable candidate sets and proved inexpensive and stable.

    Conclusions:

    • CUI-Curate provides a scalable and cost-efficient method for generating UMLS concept sets.
    • The framework is suitable for clinical NLP and phenotyping applications, offering clinician-reviewable outputs.
    • Automated concept set curation using GraphRAG significantly improves upon traditional methods.