A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes.

ABSTRACT:

Background

Emerging evidence indicates an elevated risk of post-concussion musculoskeletal injuries in collegiate athletes; however, identifying athletes at highest risk remains to be elucidated.

Objective

The purpose of this study was to model post-concussion musculoskeletal injury risk in collegiate athletes by integrating a comprehensive set of variables by machine learning.

Methods

A risk model was developed and tested on a dataset of 194 athletes (155 in the training set and 39 in the test set) with 135 variables entered into the analysis, which included participant's heath and athletic history, concussion injury and recovery-specific criteria, and outcomes from a diverse array of concussion assessments. The machine learning approach involved transforming variabl

SUBMITTER: Claros CC 

PROVIDER: S-EPMC12460441 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature