Predicting blood transfusion following traumatic injury using machine learning models: A systematic review and narrative synthesis.

Journal: The journal of trauma and acute care surgery
PMID: 38720200

Abstract

BACKGROUND: Hemorrhage is a leading cause of preventable death in trauma. Accurately predicting a patient's blood transfusion requirement is essential but can be difficult. Machine learning (ML) is a field of artificial intelligence that is emerging within medicine for accurate prediction modeling. This systematic review aimed to identify and evaluate all ML models that predict blood transfusion in trauma.

Authors

  • William Oakley
    From the Centre for Trauma Sciences (W.O., M.M.), Blizard Institute, Queen Mary University of London; and Barts Health NHS Trust (S.T., Z.P.), London, United Kingdom.
  • Sankalp Tandle
    Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, E1 2AT, UK. sankalp.tandle1@nhs.net.
  • Zane Perkins
    Centre for Trauma Sciences, Queen Mary University of London, London, UK.
  • Max Marsden
    Centre for Trauma Sciences, Queen Mary, University of London. London, UK.

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