Development of a field artificial intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds.

Journal: The journal of trauma and acute care surgery
PMID: 34016929

Abstract

BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome these limitations by accurately and confidently making predictions to support in-field triage in the first hours after traumatic injury.

Authors

  • Charlie J Nederpelt
    From the Division of Trauma, Emergency Surgery and Surgical Critical Care (TESSC) (C.J.N., A.K.M., O.A., J.A.F., J.J.P., A.E.M., P.J.F., H.M.A.K., D.R.K., G.C.V., N.S.), Massachusetts General Hospital (MGH), Boston, Massachusetts; Department of Trauma Surgery (C.J.N.), Leiden University Medical Center, Leiden, The Netherlands; Lincoln Laboratory (T.T., J.R., M.C.), Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts; and Center for Outcomes and Patient Safety in Surgery (H.M.A.K), Massachusetts General Hospital (MGH), Boston, Massachusetts.
  • Ava K Mokhtari
  • Osaid Alser
  • Theodoros Tsiligkaridis
    MIT Lincoln Laboratory, Lexington, MA 02421, United States of America. Electronic address: ttsili@ll.mit.edu.
  • Jay Roberts
  • Miriam Cha
  • Jason A Fawley
  • Jonathan J Parks
    DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida.
  • April E Mendoza
  • Peter J Fagenholz
  • Haytham M A Kaafarani
    Massachusetts General Hospital & Harvard Medical School, Boston, MA.
  • David R King
  • George C Velmahos
    Massachusetts General Hospital & Harvard Medical School, Boston, MA.
  • Noelle Saillant

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