An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model Version 5.0
- Resource Type
- Authors
- Chunhua Weng; Vojtech Huser; Anando Sen; Gregory W. Hruby; David J. Albers; Alexander Rusanov; Shreya Chakrabarti
- Source
- Journal of Healthcare Informatics Research. 1:1-18
- Subject
- Computer science
business.industry
030503 health policy & services
Interoperability
Health Informatics
Computational intelligence
Retrospective cohort study
External Data Representation
computer.software_genre
Health informatics
Article
Field (computer science)
Computer Science Applications
03 medical and health sciences
Identification (information)
0302 clinical medicine
Artificial Intelligence
ComputingMilieux_COMPUTERSANDSOCIETY
Observational study
030212 general & internal medicine
Data mining
0305 other medical science
business
computer
Information Systems
- Language
- ISSN
- 2509-498X
2509-4971
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 algorithm achieves an average Area Under the Curve (AUC) of 0.966 and an average Precision at 5 of 0.983. This interoperable method promises to achieve efficient cohort identification in EHR databases. We discuss suitable applications of our method and its limitations and propose warranted future work.