Unsupervised discovery of clinical disease signatures using probabilistic independence.

Journal: Journal of biomedical informatics
Published Date: Apr 23, 2025

Abstract

OBJECTIVE: This study uses probabilistic independence to disentangle patient-specific sources of disease and their signatures in Electronic Health Record (EHR) data.

Authors

  • Thomas A Lasko
    Vanderbilt University School of Medicine, Nashville, TN.
  • William W Stead
    McKesson Foundation, Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.
  • John M Still
    Vanderbilt University Medical Center, 1211 Medical Center Dr, Nashville, TN 37232, USA.
  • Thomas Z Li
    Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
  • Michael Kammer
    Vanderbilt University Medical Center, Nashville, TN, 37235, USA.
  • Marco Barbero-Mota
    Vanderbilt University Medical Center, 1211 Medical Center Dr, Nashville, TN 37232, USA.
  • Eric V Strobl
    Department of Psychiatry & Behavioral Sciences, 1601 23rd Avenue South, Nashville, 37232, TN, United States of America. Electronic address: eric.strobl@vumc.org.
  • Bennett A Landman
    Vanderbilt University, Nashville TN 37235, USA.
  • Fabien Maldonado
    Mechanical Engineering Department, Vanderbilt University, Nashville, TN, USA.

Keywords

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