Using Machine Learning to Predict Lower-Extremity Injury in US Special Forces.

Journal: Medicine and science in sports and exercise

PMID: 30985586

Abstract

INTRODUCTION: Musculoskeletal injury rates in military personnel remain unacceptably high. Application of machine learning algorithms could be useful in multivariate models to predict injury in this population. The purpose of this study was to investigate if interaction between individual predictors, using a decision tree model, could be used to develop a population-specific algorithm of lower-extremity injury (LEI) risk.

Authors

Chris Connaboy

Neuromuscular Research Laboratory/Warrior Human Performance Research Center, Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, PA.
Shawn R Eagle
Caleb D Johnson
Shawn D Flanagan
Q I Mi
Bradley C Nindl

Keywords

Adult Algorithms Athletic Injuries Body Composition Humans Knee Joint Lower Extremity Machine Learning Military Personnel Muscle Strength Oxygen Consumption Predictive Value of Tests Prospective Studies Range of Motion, Articular Risk Factors Young Adult

External Resources

View on PubMed Access via DOI PubMed (30985586)

Using Machine Learning to Predict Lower-Extremity Injury in US Special Forces.

Abstract

Authors

Keywords

External Resources

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