parsing-trial-eligibility

You can screen synthetic patients by asking the Skill to parse a ClinicalTrials.gov eligibility block and compare it against a patient profile, generating an explainable pass, fail, or unknown result for every individual requirement.

Clinical text parsing handles negation and temporality awareness by extracting clinical entities, applying threshold and unit handling, and structuring typed predicates to accurately evaluate conditions, medications, and laboratory values within eligibility criteria.

Yes, ClinicalTrials.gov study screening supports synthetic patient evaluation by converting free-text eligibility blocks into structured inclusion and exclusion logic, then comparing those rules against synthetic patient facts to generate candidate eligibility reports.

The best way to automate inclusion exclusion criteria extraction is to separate the sections and convert individual requirements into typed predicates for conditions, demographics, medications, and performance status, creating structured rules for patient matching.

Patient screening reports unknown results when missing information prevents a definitive evaluation, highlighting gaps in patient facts so clinicians can review candidate eligibility rather than relying on autonomous enrollment decisions.