An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model version 5.0.

ABSTRACT: Cohort identification for clinical studies tends to be laborious, time-consuming, and expensive. Developing automated or semi-automated methods for cohort identification is one of the "holy grails" in the field of biomedical informatics. We propose a high-throughput similarity-based cohort identification algorithm by applying numerical abstractions on Electronic Health Records (EHR) data. We implement this algorithm using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which enables sites using this standardized EHR data representation to avail this algorithm with minimum effort for local implementation. We validate its performance for a retrospective cohort identification task on six clinical trials conducted at the Columbia University Medical Center. Our al

SUBMITTER: Chakrabarti S 

PROVIDER: S-EPMC5536903 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature