Characterizing Sub-Cohorts via Data Normalization and Representation Learning

Rush III, Everett; Ozmen, Ozgur; Knight, Kathryn; Park, Byung; Baker, Clifton; Jones, Makoto; Ward, Merry; Nebeker, Jonathan R.

Title: Characterizing Sub-Cohorts via Data Normalization and Representation Learning

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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.

Authors:

^[1];

^[1]; Knight, Kathryn ^[1];

^[1]; Baker, Clifton ^[2]; Jones, Makoto ^[2]; Ward, Merry ^[2]; Nebeker, Jonathan R. ^[2]

ORNL
Department of Veterans Affairs

Publication Date:: 2020-07-01

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1659604

DOE Contract Number:: AC05-00OR22725

Resource Type:: Conference

Resource Relation:: Conference: IEEE International Symposium on Computer Based Medical Systems (CBMS) - Rochester, Minnesota, United States of America - 7/28/2020 4:00:00 AM-7/30/2020 4:00:00 AM

Country of Publication:: United States

Language:: English

Citation Formats


                    Rush III, Everett, Ozmen, Ozgur, Knight, Kathryn, Park, Byung, Baker, Clifton, Jones, Makoto, Ward, Merry, and Nebeker, Jonathan R. Characterizing Sub-Cohorts via Data Normalization and Representation Learning.  United States: N. p., 2020. 
        Web.

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                    Rush III, Everett, Ozmen, Ozgur, Knight, Kathryn, Park, Byung, Baker, Clifton, Jones, Makoto, Ward, Merry, & Nebeker, Jonathan R. Characterizing Sub-Cohorts via Data Normalization and Representation Learning.  United States.

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                    Rush III, Everett, Ozmen, Ozgur, Knight, Kathryn, Park, Byung, Baker, Clifton, Jones, Makoto, Ward, Merry, and Nebeker, Jonathan R. 2020.  
        "Characterizing Sub-Cohorts via Data Normalization and Representation Learning".  United States.  https://www.osti.gov/servlets/purl/1659604.

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@article{osti_1659604,

  title        = {Characterizing Sub-Cohorts via Data Normalization and Representation Learning},

  author       = {Rush III, Everett and Ozmen, Ozgur and Knight, Kathryn and Park, Byung and Baker, Clifton and Jones, Makoto and Ward, Merry and Nebeker, Jonathan R.},

  abstractNote = {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.},

  doi          = {},

  url          = {https://www.osti.gov/biblio/1659604},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2020},

  month        = {7}

}

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