Machine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design.

ABSTRACT: Overly restrictive eligibility criteria for clinical trials may limit the generalizability of the trial results to their target real-world patient populations. We developed a novel machine learning approach using large collections of real-world data (RWD) to better inform clinical trial eligibility criteria design. We extracted patients' clinical events from electronic health records (EHRs), which include demographics, diagnoses, and drugs, and assumed certain compositions of these clinical events within an individual's EHRs can determine the subphenotypes-homogeneous clusters of patients, where patients within each subgroup share similar clinical characteristics. We introduced an outcome-guided probabilistic model to identify those subphenotypes, such that the patients within the same sub

SUBMITTER: Xu J 

PROVIDER: S-EPMC9837131 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature