Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study.

Journal: The journal of trauma and acute care surgery
Published Date: Jul 1, 2019

Abstract

BACKGROUND: Trauma has long been considered unpredictable. Artificial neural networks (ANN) have recently shown the ability to predict admission volume, acuity, and operative needs at a single trauma center with very high reliability. This model has not been tested in a multicenter model with differing climate and geography. We hypothesize that an ANN can accurately predict trauma admission volume, penetrating trauma admissions, and mean Injury Severity Score (ISS) with a high degree of reliability across multiple trauma centers.

Authors

  • Bradley M Dennis
  • David P Stonko
    Vanderbilt University School of Medicine, Nashville, TN, USA.
  • Rachael A Callcut
    Division of General Surgery, Department of Surgery, School of Medicine, University of California San Francisco, San Francisco, California, United States of America.
  • Richard A Sidwell
  • Nicole A Stassen
  • Mitchell J Cohen
    Division of General Surgery, Department of Surgery, School of Medicine, University of California San Francisco, San Francisco, California, United States of America.
  • Bryan A Cotton
    Center for Translational Injury Research, Division of Acute Care Surgery, Department of Surgery, Medical School, University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
  • Oscar D Guillamondegui

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