Predictive Modeling of Sports-Related Concussions using Clinical Assessment Metrics
Concussions represent a growing health concern that are challenging to diagnose and manage. Roughly four million concussions are diagnosed every year in the United States. While research in machine learning applications for concussions has focused on using advanced metrics such neuroimaging techniques and blood biomarkers, these metrics are yet to be implemented at a clinical level due to cost and reliability concerns. Therefore, concussion diagnosis is still reliant on clinical evaluations of symptoms, balance, and neurocognitive status and function. The lack of a universal threshold on these assessments makes the diagnosis process reliant on a physician's interpretation of these assessment scores. This study aims to explore the use of machine learning techniques to aid the concussion diagnosis process. These models could provide an automated means to flag concussed patients even before being seen by a doctor as well as expand the scope of concussion diagnosis to remote locations and areas with limited access to doctors.
S. Subhash et al., "Predictive Modeling of Sports-Related Concussions using Clinical Assessment Metrics," Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (2020, Canberra, Australia), pp. 513 - 520, Institute of Electrical and Electronics Engineers (IEEE), Jan 2021.
The definitive version is available at https://doi.org/10.1109/SSCI47803.2020.9308473
2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (2020: Dec. 1-4, Canberra, Australia)
Electrical and Computer Engineering
Mathematics and Statistics
Center for High Performance Computing Research
Keywords and Phrases
Classification Models; Concussions; Predictive Modeling
International Standard Book Number (ISBN)
Article - Conference proceedings
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05 Jan 2021