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Related Experiment Video

Updated: Jun 12, 2025

An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System

An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System

Published on: January 12, 2011

A Machine Learning Approach to Concussion Risk Estimation Among Players Exhibiting Visible Signs in Professional

Jared M Bruce^1,2,3, Kaitlin E Riegler⁴, Willem Meeuwisse⁵

¹Department of Biomedical and Health Informatics, University of Missouri-Kansas City School of Medicine, Kansas City, MO, 64108, USA. brucejm@umkc.edu.

Sports Medicine (Auckland, N.Z.)

|September 17, 2024

Summary

This summary is machine-generated.

Concussion history significantly increases the risk of diagnosis in professional hockey players, even beyond visible signs. Machine learning models incorporating this history improve concussion identification accuracy.

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Last Updated: Jun 12, 2025

An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System

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Published on: January 12, 2011

In Vivo Protocol of Controlled Subconcussive Head Impacts for the Validation of Field Study Data

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Published on: April 18, 2019

A Neuroscientific Approach to the Examination of Concussions in Student-Athletes

A Neuroscientific Approach to the Examination of Concussions in Student-Athletes

Published on: December 8, 2014

Area of Science:

Sports Medicine
Data Science in Sports
Injury Epidemiology

Background:

Identifying concussion risk factors like visible signs and injury mechanisms is crucial for accurate diagnosis.
Individual factors, such as a history of concussions, can enhance existing concussion risk models.

Purpose of the Study:

To develop a machine learning concussion risk model for professional hockey players with visible signs.
To evaluate if including prior concussion history improves model performance.

Main Methods:

Utilized National Hockey League (NHL) spotter data (2018-2022) on visible signs and injury mechanisms.
Matched spotter events with medical records to confirm physician-diagnosed concussions.
Compared machine learning models: conditional inference tree, random forest, and logistic regression.

Main Results:

Models demonstrated high performance (AUC 0.79-0.82) in identifying concussions.
Concussion history was a significant factor in predictive models.
Each prior concussion increased the odds of a new diagnosis by 1.32 times.

Conclusions:

Developed simple tree and logistic algorithms for concussion screening and diagnostic support.
Player concussion history provides valuable risk information beyond visible signs and injury mechanisms.