Predictive Modeling of Sports-Related Concussions using Clinical Assessment Metrics

Abstract

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.

Meeting Name

2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (2020: Dec. 1-4, Canberra, Australia)

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Third Department

Mathematics and Statistics

Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Classification Models; Concussions; Predictive Modeling

International Standard Book Number (ISBN)

978-172812547-3

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

05 Jan 2021

Link to Full Text

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