Continuous-time probabilistic models for longitudinal electronic health records.

Journal: Journal of biomedical informatics
Published Date: May 7, 2022

Abstract

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. We present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time. This method works with arbitrary sampling patterns and captures the joint probability distribution between variable measurements and the time intervals between them. Inference algorithms are derived that can be used to evaluate the likelihood of future using under a trained model. As an example, we consider data from the United States Veterans Health Administration (VHA) in the areas of diabetes and depression. Likelihood ratio maps are produced showing the likelihood of risk for moderate-severe vs minimal depression as measured by the Patient Health Questionnaire-9 (PHQ-9).

Authors

  • Alan D Kaplan
    Computational Engineering Division, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550, USA. Electronic address: kaplan7@llnl.gov.
  • Uttara Tipnis
    Computational Engineering Division, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550, USA.
  • Jean C Beckham
    Durham Veterans Affairs (VA) Health Care System, Durham, NC, USA; VA Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA; Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
  • Nathan A Kimbrel
    Durham Veterans Affairs (VA) Health Care System, Durham, NC, USA; VA Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA; Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; VA Health Services Research and Development Center of Innovation to Accelerate Discovery and Practice Transformation, Durham, NC, USA.
  • David W Oslin
    CPL. Michael J. Crescenz VA Medical Center (Philadelphia), Perelman School of Medicine, University of Pennsylvania Philadelphia, PA, United States.
  • Benjamin H McMahon
    Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA.

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