Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation

Angelo Ziletti; Leonardo D'Ambrosi

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arXiv · 2502.21107

Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation

Angelo Ziletti·Leonardo D'Ambrosi

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Abstract

Clinical cohort definition is crucial for patient recruitment and observational studies, yet translating inclusion/exclusion criteria into SQL queries remains challenging and manual. We present an automated system utilizing large language models that combines criteria parsing, two-level retrieval augmented generation with specialized knowledge bases, medical concept standardization, and SQL generation to retrieve patient cohorts with patient funnels. The system achieves 0.75 F1-score in cohort identification on EHR data, effectively capturing complex temporal and logical relationships. These results demonstrate the feasibility of automated cohort generation for epidemiological research.

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Angelo Ziletti, Leonardo D'Ambrosi. 2025-02-28. Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation. https://arxiv.org/abs/2502.21107

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