Related Experiment Video
Updated: Nov 24, 2025
A Neuroscientific Approach to the Examination of Concussions in Student-Athletes
Published on: December 8, 2014
Predicting Risk of Sport-Related Concussion in Collegiate Athletes and Military Cadets: A Machine Learning Approach
Joel Castellanos1,2, Cheng Perng Phoo3,4, James T Eckner5
1Department of Physical Medicine and Rehabilitation, Michigan Medicine, University of Michigan, 325 E. Eisenhower Parkway, Ann Arbor, MI, 48108, USA.
Objective:
To develop a predictive model for sport-related concussion in collegiate athletes and military service academy cadets using baseline data collecting during the pre-participation examination.
Methods:
Baseline assessments were performed in 15,682 participants from 21 US academic institutions and military service academies participating in the CARE Consortium Study during the 2015-2016 academic year. Participants were monitored for sport-related concussion during the subsequent season. 176 baseline covariates mapped to 957 binary features were used as input into a support vector machine model with the goal of learning to stratify participants according to their risk for sport-related concussion. Performance was evaluated in terms of area under the receiver operating characteristic curve (AUROC) on a held-out test set. Model inputs significantly associated with either increased or decreased risk were identified.
Results:
595 participants (3.79%) sustained a concussion during the study period. The predictive model achieved an AUROC of 0.73 (95% confidence interval 0.70-0.76), with variable performance across sports. Features with significant positive and negative associations with subsequent sport-related concussion were identified.
Conclusion(S):
This predictive model using only baseline data identified athletes and cadets who would go on to sustain sport-related concussion with comparable accuracy to many existing concussion assessment tools for identifying concussion. Furthermore, this study provides insight into potential concussion risk and protective factors.
