A practical approach to predicting long-term outcomes in traumatic brain injury: Enhancing clinical decision-making with machine learning.

Journal: Computers in biology and medicine
Published Date: Jul 23, 2025

Abstract

BACKGROUND: Traumatic brain injury (TBI) is among the most prevalent causes of emergency department visits globally. TBI leads to high morbidity and mortality rates, which poses a noteworthy burden on the medical system regarding both patients and economics. In this study, we aimed to enhance clinical decision-making and resource allocation by predicting the six-month outcome of patients with TBI based on an extended Glasgow outcome scale using CatBoost, a deep-learning model based on gradient-boosted decision trees.

Authors

  • Amirmohammad Farrokhi
    Medical School, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Mahtab Jalali
    Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Mohamed Sobhi Jabal
    Department of Radiology, Mayo Clinic, Rochester, MN, United States. Electronic address: jabal.mohamedsobhi@mayo.edu.
  • Saeed Abdollahifard
    Medical School, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Reza Taheri
    Neurosurgery Department, Shiraz Medical School, Shiraz University of Medical Sciences, Shiraz, Iran. reza.neuro@gmail.com.
  • Omid Yousefi
    Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Amin Niakan
    Trauma Research Center, Department of Neurosurgery, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Hosseinali Khalili

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