@inproceedings{shen-etal-2026-patient,
    title = "Patient-Similarity Cohort Reasoning in Clinical Text-to-{SQL}",
    author = "Shen, Yifei  and
      Zhao, Yilun  and
      Ou, Justice  and
      Huang, Tinglin  and
      Cohan, Arman",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.64/",
    pages = "1367--1412",
    ISBN = "979-8-89176-380-7",
    abstract = "Real-world clinical text-to-SQL requires reasoning over heterogeneous EHR tables, temporal windows, and patient-similarity cohorts to produce executable queries. We introduce ClinSQL, a benchmark of 633 expert-annotated tasks on MIMIC-IV v3.1 that demands multi-table joins, clinically meaningful filters, and executable SQL. Solving ClinSQL entails navigating schema metadata and clinical coding systems, handling long contexts, and composing multi-step queries beyond traditional text-to-SQL. We evaluate 20 proprietary and open-source models under Chain-of-Thought self-refinement and use rubric-based SQL analysis with execution checks that prioritize critical clinical requirements. Despite recent advances, performance remains far from clinical reliability: on the test set, GPT-5-mini attains 74.7{\%} execution score, DeepSeek-R1 leads open-source at 69.2{\%} and Gemini-2.5-Pro drops from 85.5{\%} on Easy to 67.2{\%} on Hard. Progress on ClinSQL marks tangible advances toward clinically reliable text-to-SQL for real-world EHR analytics."
}