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Current neurology and neuroscience reports
Feb 19, 2025;25 (1): 19.

Machine Learning Approaches to Prognostication in Traumatic Brain Injury.

Neeraj Badjatia, Jamie Podell, Ryan B Felix, Lujie Karen Chen, Kenneth Dalton, Tina I Wang, Shiming Yang, Peter Hu
Author Information
  1. Neeraj Badjatia: Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA. nbadjatia@som.umaryland.edu. ORCID
  2. Jamie Podell: Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA.
  3. Ryan B Felix: Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA. ORCID
  4. Lujie Karen Chen: Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA. ORCID
  5. Kenneth Dalton: Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA.
  6. Tina I Wang: Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, USA. ORCID
  7. Shiming Yang: Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA. ORCID
  8. Peter Hu: Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA. ORCID
PMID: 39969697 DOI: 10.1007/s11910-025-01405-x

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.
RECENT FINDINGS: ML algorithms of clinical features, neuroimaging, and metrics from the autonomic nervous system enhance the early detection of clinical deterioration and improve outcome prediction. Challenges persist, including issues of data variability, model interpretability, and overfitting. However, advancements in model standardization and validation are key to enhancing their clinical applicability. ML-based, multimodal approaches offer transformative potential for personalized treatment planning and patient management. Future directions include integrating digital twins and real-time continuous data analysis, reinforcing the idea that comprehensive data amalgamation is essential for precise, adaptive prognostication and decision-making in neurocritical care, ultimately leading to better patient outcomes.

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Grants

  1. MTEC - 20 - 13 - IMAS - 006/U.S. Department of Defense
  2. W81XWH-24-CCCRP1/Congressionally Directed Medical Research Programs
  3. W81XWH-24-CCCRP1/Congressionally Directed Medical Research Programs
  4. W81XWH-24-CCCRP1/Congressionally Directed Medical Research Programs
  5. HR001122S0043RITMO-PA-006/Defense Advanced Research Projects Agency
  6. HR001122S0043RITMO-PA-006/Defense Advanced Research Projects Agency
  7. HR001122S0043RITMO-PA-006/Defense Advanced Research Projects Agency

MeSH Term

Humans
Machine Learning
Brain Injuries, Traumatic
Prognosis
Neuroimaging
Journal Article Review
Article has an altmetric score of 2

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