| Title |
An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model Version 5.0
|
|---|---|
| Published in |
Journal of Healthcare Informatics Research, June 2017
|
| DOI | 10.1007/s41666-017-0005-6 |
| Pubmed ID | |
| Authors |
Shreya Chakrabarti, Anando Sen, Vojtech Huser, Gregory W. Hruby, Alexander Rusanov, David J. Albers, Chunhua Weng |
| 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 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. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 23% |
| Student > Ph. D. Student | 6 | 19% |
| Student > Master | 3 | 10% |
| Other | 2 | 6% |
| Professor | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 9 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 11 | 35% |
| Engineering | 4 | 13% |
| Mathematics | 2 | 6% |
| Medicine and Dentistry | 1 | 3% |
| Biochemistry, Genetics and Molecular Biology | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 12 | 39% |
Attention Score in Context
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