Agentic Authoring of OMOP Concept Sets from Natural Language
Chen, H.; He, X.; Dai, H.; Huang, Y.; Liu, M.; Bian, J.
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Authoring OMOP concept sets from free-text descriptions remains a major bottleneck in scalable computable phenotyping for observational research. Existing tools support parts of this workflow but are designed primarily for interactive expert use rather than autonomous large language model (LLM) agents. We present an agentic framework that automatically generates OMOP concept sets by combining vocabulary tools, ontology extensions (RxClass, LOINC, and Disease Ontology), and procedural guidance. In ablation studies, the best configuration achieved Recall@100 of 0.965 and AP@100 of 0.875 on the development set. Cohort-level validation against OMOP-mapped EHR data yielded precision of 0.970, recall of 0.998, and a Jaccard index of 0.968. On an independent silver-standard benchmark of 457 concept-vocabulary pairs from 15 AD/ADRD target trial emulation studies, Recall@100 reached 0.835 and AP@100 reached 0.786. Task-specific tools outperformed unrestricted SQL access and PHOEBE 2.0, while progressive guidance performed best.
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