Machine-Learning Model Predicts Recovery Time from Sports-Related Concussions
The prevalence of concussions in sports is well known. So, too, is the challenge clinicians and others face when they have to decide when an athlete can return to the game after a head injury. While most athletes recover from a sports-related concussion in about seven to 10 days, some need more time. This predicament makes managing the treatment of sports-related concussions very complicated.
Researchers from Florida Atlantic University’s College of Engineering and Computer Science and Sivotec Analytics in Boca Raton, and collaborators, have come up with a novel solution. They are teaching machines how to predict recovery time from sports-related concussions based on symptoms like headache, dizziness, and fatigue. Their study, “Machine Learning in Modeling High School Sport Concussion Symptom Resolve” published in Medicine & Science in Sports & Exercise, can be used as the foundation for a decision support system that would aid clinicians in developing individualized treatment for injured athletes. This research also is part of a larger ongoing effort by the team to develop machine learning models to help diagnose, track, and treat a variety of brain health issues.
Using data from the National Athletic Treatment, Injury and Outcomes Network, an injury surveillance program on high school student-athletes, the researchers examined data on 2,004 concussion incidents in 22 sports, looking at where the injuries primarily occurred. They found that more than half of the concussions happened in American football.
With this information, they created a new dataset of concussive injuries in football as well as other contact sports that included wrestling, field hockey, as well as boys’ and girls’ basketball, soccer, and lacrosse. This new dataset included 922 football concussions and 689 concussions from other contact sports, totaling 1,611 concussion incidents from all contact sports. For the dataset of all contact sports, total number of symptoms reported per sports-related concussion incident ranged from zero to 17, with 55 percent of the student-athletes reporting five or more symptoms.
The researchers implemented a supervised machine learning-based modeling approach to predict recovery time of concussion-related symptoms within seven, 14, and 28 days. They examined the efficacy of 10 classification algorithms in building the prediction models, using the dataset representing three years of concussions suffered by these high school student-athletes in football and the other contact sports.
With the dataset showing that the most prevalent reported sports-related concussion symptom was a headache (94.9 percent), followed by dizziness (74.
