Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept.

Journal: European journal of trauma and emergency surgery : official publication of the European Trauma Society
PMID: 38265444

Abstract

PURPOSE: Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan system failure, and number of blood products used. Various prediction tools have been developed for the initiation of MHP, but no single tool has demonstrated strong prediction with early clinical data. We sought to develop a massive transfusion prediction model using machine learning and early clinical data.

Authors

  • Anton Nikouline
    Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
  • Jinyue Feng
    Department of Computer Science, University of Toronto, Toronto, ON, Canada.
  • Frank Rudzicz
    University of Toronto, Toronto, Canada.
  • Avery Nathens
    Division of General Surgery, Department of Surgery, University of Toronto, Toronto, Canada.
  • Brodie Nolan
    Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Emergency Medicine, St. Michael's Hospital, Toronto, Ontario, Canada.

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