Machine learning functional impairment classification with electronic health record data.

Journal: Journal of the American Geriatrics Society
PMID: 37195174

Abstract

BACKGROUND: Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment.

Authors

  • Juliessa M Pavon
    Department of Medicine/Division of Geriatrics, Duke University, Durham, North Carolina, USA.
  • Laura Previll
    Department of Medicine/Division of Geriatrics, Duke University, Durham, North Carolina, USA.
  • Myung Woo
    AI Health, Duke University, Durham, North Carolina, USA.
  • Ricardo Henao
    Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina.
  • Mary Solomon
    AI Health, Duke University, Durham, North Carolina, USA.
  • Ursula Rogers
    AI Health, Duke University, Durham, North Carolina, USA.
  • Andrew Olson
    Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
  • Jonathan Fischer
    Department of Community and Family Medicine, Duke University, Durham, North Carolina, USA.
  • Christopher Leo
    Department of Medicine/Division of Geriatrics, Duke University, Durham, North Carolina, USA.
  • Gerda Fillenbaum
    Claude D. Pepper Center, Duke University, Durham, North Carolina, USA.
  • Helen Hoenig
    Department of Medicine/Division of Geriatrics, Duke University, Durham, North Carolina, USA.
  • David Casarett
    Department of Medicine/Division of General Internal Medicine/Palliative Care, Duke University, Durham, North Carolina, USA.

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