Computational Framework for Structuring and Analyzing Clinical Trial Criteria for AI-Guided Fine-grained Matching.

ABSTRACT: While artificial intelligence (AI) has demonstrated potential in automating clinical trial matching, most existing solutions rely on high-level structured data or oversimplified criteria. This study introduces a framework to structure and analyze eligibility criteria across three real-world trial protocols, aiming to inform more granular AI-driven trial matching strategies. Trial criteria from three protocols were decomposed into individual variables and evaluated based on data type, scope, and dependency. Complexity was assessed using a novel formula incorporating the number of independent and dependent variables, alongside the Flesch-Kincaid reading grade level. Quantitative analysis explored variation across trials. Protocols contained between 22-160 eligibility variables, with 4-22% sh

SUBMITTER: Habib DRS 

PROVIDER: S-EPMC12640310 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature