← 研踪 PaperTrailarXiv:2611.10681 · v1
Formalizing Patient-Set Semantics for OMOP CDM-Based Clinical Trial Feasibility Assessment:A Neuro-Symbolic Methodology for Traceable Site-Feasibility Patient Counts
首次提交 2026-11-07 19:54
版本更新 2026-11-07 19:54
雷达观测 2026-11-10 11:14
摘要
This study examines the interpretation and execution of natural-language eligibility criteria for clinical trial site-feasibility patient-count estimation using the OMOP Common Data Model (CDM). It treats each count as the size of a patient set produced under explicitly declared choices about clinical concepts, temporal conditions, exclusions, proxies, and available data. The study presents NS-CoT, a neuro-symbolic pipeline that uses an LLM to interpret and structure criteria, followed by symbolic processing and deterministic SQL compilation. Intermediate representations and execution records document how criteria are translated into patient sets and counts. Evaluation uses synthetic OMOP CDM data, controlled benchmarks, and a multi-site simulation. Comparisons with a direct-SQL baseline examine execution success, count agreement, patient-set agreement, and preservation of reference-defined site-feasibility labels and rankings. Performance varies across evaluation settings. Layer-wise inspection also supports the identification of implementation and policy errors, followed by correction and re-evaluation. The work investigates the reviewability and fidelity of cohort computation relative to declared reference definitions. It does not establish that these definitions capture a unique clinical ground truth or demonstrate improved recruitment, site selection, or trial outcomes in practice.
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v1 · 2026-11-07 19:54 · 最新观测版本
Formalizing Patient-Set Semantics for OMOP CDM-Based Clinical Trial Feasibility Assessment:A Neuro-Symbolic Methodology for Traceable Site-Feasibility Patient Counts
This study examines the interpretation and execution of natural-language eligibility criteria for clinical trial site-feasibility patient-count estimation using the OMOP Common Data Model (CDM). It treats each count as the size of a patient set produced under explicitly declared choices about clinical concepts, temporal conditions, exclusions, proxies, and available data. The study presents NS-CoT, a neuro-symbolic pipeline that uses an LLM to interpret and structure criteria, followed by symbolic processing and deterministic SQL compilation. Intermediate representations and execution records document how criteria are translated into patient sets and counts. Evaluation uses synthetic OMOP CDM data, controlled benchmarks, and a multi-site simulation. Comparisons with a direct-SQL baseline examine execution success, count agreement, patient-set agreement, and preservation of reference-defined site-feasibility labels and rankings. Performance varies across evaluation settings. Layer-wise inspection also supports the identification of implementation and policy errors, followed by correction and re-evaluation. The work investigates the reviewability and fidelity of cohort computation relative to declared reference definitions. It does not establish that these definitions capture a unique clinical ground truth or demonstrate improved recruitment, site selection, or trial outcomes in practice.
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