Machine learning to predict passenger mortality and hospital length of stay following motor vehicle collision.

Journal: Neurosurgical focus
Published Date: Apr 1, 2022

Abstract

OBJECTIVE: Motor vehicle collisions (MVCs) account for 1.35 million deaths and cost $518 billion US dollars each year worldwide, disproportionately affecting young patients and low-income nations. The ability to successfully anticipate clinical outcomes will help physicians form effective management strategies and counsel families with greater accuracy. The authors aimed to train several classifiers, including a neural network model, to accurately predict MVC outcomes.

Authors

  • John Paul G Kolcun
    Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida.
  • Brian Covello
    2Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida; and.
  • Joanna E Gernsback
    3Department of Neurosurgery, Oklahoma University, Oklahoma City, Oklahoma.
  • Iahn Cajigas
    Department of Physical Medicine and Rehabilitation, Harvard Medical School, 300 First Avenue, Charlestown, MA 02129, USA.
  • Jonathan R Jagid
    Department of Neurological Surgery, University of Miami School of Medicine, Miami, FL 33136, United States of America.

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