Machine Learning Approaches to Prognostication in Traumatic Brain Injury.

Journal: Current neurology and neuroscience reports
PMID: 39969697

Abstract

PURPOSE OF REVIEW: This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data-including clinical, imaging, and physiological inputs-to identify intricate non-linear relationships that traditional methods might overlook.

Authors

  • Neeraj Badjatia
    Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA. nbadjatia@som.umaryland.edu.
  • Jamie Podell
    Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Ryan B Felix
    Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Lujie Karen Chen
    Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA.
  • Kenneth Dalton
    Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Tina I Wang
    Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Shiming Yang
    University of Maryland School of Medicine, Baltimore, MD 21201, United States.
  • Peter Hu
    Janssen Research & Development, LLC, Raritan, New Jersey, United States of America.

Keywords

External Resources

Popular Topics
Recent Journals