Predicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study.

Journal: Geriatrics & gerontology international
PMID: 39800578

Abstract

AIM: Pre-injury frailty has been investigated as a tool to predict outcomes of older trauma patients. Using artificial intelligence principles of machine learning, we aimed to identify a "signature" (combination of clinical variables) that could predict which older adults are at risk of fall-related hospital admission. We hypothesized that frailty, measured using the 5-item modified Frailty Index, could be utilized in combination with other factors as a predictor of admission for fall-related injuries.

Authors

  • Nam Le
    Iowa Initiative for Artificial Intelligence, University of Iowa, Iowa City, Iowa, USA.
  • Milan Sonka
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, United States; Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, 52242, United States.
  • Dionne A Skeete
    Division of Acute Care Surgery, Department of Surgery, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, Iowa, USA.
  • Kathleen S Romanowski
    Division of Burn Surgery, University of California, Davis Medical Center and Shriners Children's Northern California, Sacramento, California, USA.
  • Colette Galet
    Division of Acute Care Surgery, Department of Surgery, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, Iowa, USA.

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