Enhancing trauma triage in low-resource settings using machine learning: a performance comparison with the Kampala Trauma Score.

Journal: BMC emergency medicine
PMID: 39849342

Abstract

BACKGROUND: Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these settings, though its predictive accuracy remains under debate. This study evaluates the effectiveness of machine learning (ML) models in predicting triage decisions and compares their performance to the KTS.

Authors

  • Mike Nsubuga
    The African Center of Excellence in Bioinformatics and Data-Intensive Science (ACE), Kampala, Uganda.
  • Timothy Mwanje Kintu
    The Infectious Diseases Institute, Makerere University, P. O. Box 22418, Kampala, Uganda.
  • Helen Please
    Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Kelsey Stewart
    Department of Surgery, Mayo Clinic, Rochester, MN, US.
  • Sergio M Navarro
    Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, Said Business School, University of Oxford, Oxford, United Kingdom.

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