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Validation of a Machine Learning Brain Electrical Activity–Based Index to Aid in Diagnosing Concussion Among Athletes

Authors
  • Bazarian, Jeffrey J.1
  • Elbin, Robert J.2
  • Casa, Douglas J.3
  • Hotz, Gillian A.4
  • Neville, Christopher5
  • Lopez, Rebecca M.6
  • Schnyer, David M.7
  • Yeargin, Susan8
  • Covassin, Tracey9
  • 1 Department of Emergency Medicine, University of Rochester School of Medicine, Rochester, New York
  • 2 Office for Sports Concussion Research, University of Arkansas, Fayetteville
  • 3 Korey Stringer Institute, University of Connecticut, Storrs
  • 4 UHealth Concussion Program, University of Miami, Miami, Florida
  • 5 Department of Physical Therapy Education, SUNY Upstate Medical University, Syracuse, New York
  • 6 Morsani College of Medicine, Orthopedics and Sports Medicine, University of South Florida, Tampa
  • 7 Department of Psychology, University of Texas at Austin, Austin
  • 8 Arnold School of Public Health, University of South Carolina, Columbia
  • 9 Department of Kinesiology, Michigan State University, East Lansing
Type
Published Article
Journal
JAMA Network Open
Publisher
American Medical Association
Publication Date
Feb 15, 2021
Volume
4
Issue
2
Identifiers
DOI: 10.1001/jamanetworkopen.2020.37349
PMID: 33587137
PMCID: PMC7885039
Source
PubMed Central
Disciplines
  • Physical Medicine and Rehabilitation
License
Unknown
External links

Abstract

This diagnostic study validates the classification accuracy of the previously derived, machine learning, multimodal, electroencephalogram-based Concussion Index in an independent cohort of athletes with concussion.

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