Characterizing Sub-Cohorts via Data Normalization and Representation Learning
| Autor: | Ozgur Ozmen, Merry Ward, Byung H. Park, Makoto Jones, Everett Rush, Kathryn Knight, Jonathan R. Nebeker, Clifton R. Baker |
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| Rok vydání: | 2020 |
| Předmět: |
020205 medical informatics
Computer science business.industry 02 engineering and technology Medical classification computer.software_genre Missing data Autoencoder Pipeline (software) Database normalization Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Cohort ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering ComputingMilieux_COMPUTERSANDSOCIETY 030212 general & internal medicine Artificial intelligence business computer Feature learning Natural language processing |
| Zdroj: | CBMS DOE / OSTI |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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