- By:
- Rush, Everett N; Ozmen, Ozgur ; Knight, Kathryn E; Park, Byung H; Baker, Clifton; Jones, Makoto; Ward, Merry; Nebeker, Jonathan
- Book Title:
- IEEE 33rd International Symposium on Computer Based Medical Systems
- Publication Date:
- November 9, 2023
- Conference Name:
- IEEE International Symposium on Computer Based Medical Systems (CBMS)
- Conference Location:
- Rochester, Minnesota, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/CBMS49503.2020.00040
Abstract
The process of identifying a cohort of interest is a very challenging task. It requires manually inspecting many patient records of complex structure that might include medical coding errors and missing data. This paper presents a computational pipeline for refining the process of cohort selection based on medical concepts recorded in the electronic health records (EHRs). The pipeline extracts EHR data for a given cohort and normalizes this data using standard vocabularies. Then a stacked denoising autoencoder is used to embed the normalized patient vectors in a low dimensional space, where the patients are subsequently clustered into sub-cohorts. The goal is to represent the cohort in a standard format and abstract variants of sub-populations. As a use-case, we applied the pipeline to 1.8 million Veterans diagnosed with major depressive disorder (MDD), and identified four meaningful sub-cohorts using the features learned by the autoencoder. Then, each sub-cohort was explored using a set of keywords for interpretation.
