A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data.

Journal: PloS one
PMID: 33606819

Abstract

BACKGROUND: Identifying individuals at risk for future hospitalization or death has been a major priority of population health management strategies. High-risk individuals are a heterogeneous group, and existing studies describing heterogeneity in high-risk individuals have been limited by data focused on clinical comorbidities and not socioeconomic or behavioral factors. We used machine learning clustering methods and linked comorbidity-based, sociodemographic, and psychobehavioral data to identify subgroups of high-risk Veterans and study long-term outcomes, hypothesizing that factors other than comorbidities would characterize several subgroups.

Authors

  • Ravi B Parikh
    Division of Hematology and Oncology, Perelman School of Medicine, University of Philadelphia, Philadelphia, Pennsylvania.
  • Kristin A Linn
    Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: klinn@upenn.edu.
  • Jiali Yan
    Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Matthew L Maciejewski
    Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina, United States of America.
  • Ann-Marie Rosland
    VA Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States of America.
  • Kevin G Volpp
    Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, United States of America.
  • Peter W Groeneveld
    From the Department of Veterans Affairs' Center for Health Equity Research and Promotion, Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA (P.W.G.); Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine, Leonard Davis Institute of Health Economics, and Center for Cardiovascular Outcomes, Quality, and Evaluative Research, University of Pennsylvania, Philadelphia (P.W.G.); University of Colorado School of Medicine, Aurora, CO (J.S.R.); Veterans Affairs Eastern Colorado Health System, Denver (J.S.R.); and American College of Cardiology, Washington, DC (J.S.R.). petergro@upenn.edu.
  • Amol S Navathe
    Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

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