Machine Learning to Predict Lower Extremity Musculoskeletal Injury Risk in Student Athletes - Henriquez, Maria; Sumner, Jacob; Faherty, Mallory; Sell, Timothy; Bent, Brinnae (Frontiers in Sports and Active Living, 2020)
Injury rates in student athletes are high and often unpredictable. Injury risk factors are not agreed upon and often not validated. Here, we present a machine learning methodology for identifying the most significant injury risk factors and develop a model of lower extremity musculoskeletal injury risk in student athletes with physical performance metrics spanning strength, postural stability, and flexibility combined with previous injury metrics. We tested our model in a population of 122 student athletes with performance metrics for the lower musculoskeletal system and achieved an injury risk accuracy of 79%. By incorporating machine learning into determining injury risk, we can increase accuracy of injury risk assessments, implement timely interventions, and decrease the number of career-ending or chronic injuries among student athletes.