Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909852841803776 |
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| author | Ziletti, Angelo D'Ambrosi, Leonardo |
| author_facet | Ziletti, Angelo D'Ambrosi, Leonardo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21107 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation Ziletti, Angelo D'Ambrosi, Leonardo Computation and Language 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. |
| title | Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.21107 |