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Lookup NU author(s): Dr Anando SenORCiD
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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.
Author(s): Chakrabarti S, Sen A, Huser V, Hruby GW, Rusanov A, Albers DJ, Weng C
Publication type: Article
Publication status: Published
Journal: Journal of Healthcare Informatics Research
Year: 2017
Volume: 1
Issue: 1
Pages: 1-18
Online publication date: 08/06/2017
Acceptance date: 19/05/2017
ISSN (print): 2509-4971
ISSN (electronic): 2509-498X
Publisher: Springer Nature
URL: https://doi.org/10.1007/s41666-017-0005-6
DOI: 10.1007/s41666-017-0005-6
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