@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL
%A Shen, Yifei
%A Zhao, Yilun
%A Ou, Justice
%A Huang, Tinglin
%A Cohan, Arman
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F shen-etal-2026-patient
%X 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.
%U https://aclanthology.org/2026.eacl-long.64/
%P 1367-1412
Markdown (Informal)
[Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL](https://aclanthology.org/2026.eacl-long.64/) (Shen et al., EACL 2026)
ACL
- Yifei Shen, Yilun Zhao, Justice Ou, Tinglin Huang, and Arman Cohan. 2026. Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1367–1412, Rabat, Morocco. Association for Computational Linguistics.