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Med Sci Sports Exerc. 2019 Jan 25. doi: 10.1249/MSS.0000000000001903. [Epub ahead of print]

Machine Learning in Modeling High School Sport Concussion Symptom Resolve.

Author information

1
SIVOTEC Analytics, Boca Raton, FL.
2
Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL.
3
Nemours Children's Hospital, Division of Neurosurgery, Orlando, FL.
4
Kerlan-Jobe Center for Sports Neurology, Los Angeles, CA.
5
Datalys Center for Sports Injury Research and Prevention, Inc., Indianapolis, IN.

Abstract

INTRODUCTION:

Concussion prevalence in Sport is well-recognized; so too is the challenge of clinical and return-to-play management for an injury with an inherent indeterminant time course of resolve. Clear, valid insight to the anticipated resolution time could assist in planning treatment intervention.

PURPOSE:

This study implemented a supervised machine learning-based approach in modeling estimated symptom resolve time in high school athletes who incurred a concussion during sport activity.

METHODS:

We examined the efficacy of 10 classification algorithms using machine learning for prediction of symptom resolution time (within seven, fourteen, or twenty-eight days), with a dataset representing three years of concussions suffered by high school student-athletes in football (most concussion incidents) and other contact sports.

RESULTS:

The most prevalent sport-related concussion reported symptom was headache (94.9%), followed by dizziness (74.3%) and difficulty concentrating (61.1%). For all three category thresholds of predicted symptom resolution time, single-factor ANOVAs revealed statistically significant performance differences across the ten classification models for all learners at a 95% confidence level (P=0.000). Naïve Bayes and Random Forest with either 100 or 500 trees were the top-performing learners with an area under the ROC curve performance ranging between 0.666 and 0.742 (0.0-1.0 scale).

CONCLUSIONS:

Considering the limitations of these data specific to symptom presentation and resolve, supervised machine learning demonstrated efficacy, while warranting further exploration, in developing symptom-based prediction models for practical estimation of sport-related concussion recovery in enhancing clinical decision support.

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