Using Machine Learning to Make Predictions in Patients Who Fall.

Journal: The Journal of surgical research
PMID: 32823009

Abstract

BACKGROUND: As the population ages, the incidence of traumatic falls has been increasing. We hypothesize that a machine learning algorithm can more accurately predict mortality after a fall compared with a standard logistic regression (LR) model based on immediately available admission data. Secondary objectives were to predict who would be discharged home and determine which variables had the largest effect on prediction.

Authors

  • Andrew J Young
    Division of Traumatology, Emergency Surgery, and Surgical Critical Care, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: andrew.young@pennmedicine.upenn.edu.
  • Allison Hare
    Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Madhu Subramanian
    Division of Traumatology, Emergency Surgery, and Surgical Critical Care, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Jessica L Weaver
    Division of Trauma, Surgical Critical Care, Burns, and Acute Care Surgery, Department of Surgery, University of California San Diego Health, San Diego, California.
  • Elinore Kaufman
    Division of Traumatology, Emergency Surgery, and Surgical Critical Care, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Carrie Sims
    Department of Trauma Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

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