{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["49(1)"],"submitter":["Habib DRS"],"pubmed_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"],"journal":["Journal of medical systems"],"pagination":["168"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12640310"],"repository":["biostudies-literature"],"pubmed_title":["Computational Framework for Structuring and Analyzing Clinical Trial Criteria for AI-Guided Fine-grained Matching."],"pmcid":["PMC12640310"],"pubmed_authors":["Evancha B","Habib DRS","Fabbri D","Mahajan I","Micheel C"],"additional_accession":[]},"is_claimable":false,"name":"Computational Framework for Structuring and Analyzing Clinical Trial Criteria for AI-Guided Fine-grained Matching.","description":"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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-05T18:47:23.073Z","creation":"2026-05-20T03:13:50.415Z"},"accession":"S-EPMC12640310","cross_references":{"pubmed":["41273484"],"doi":["10.1007/s10916-025-02303-y"]}}