TY - JOUR
T1 - Machine Learning in Modeling High School Sport Concussion Symptom Resolve
AU - Bergeron, Michael F.
AU - Landset, Sara
AU - Maugans, Todd A.
AU - Williams, Vernon B.
AU - Collins, Christy L.
AU - Wasserman, Erin B.
AU - Khoshgoftaar, Taghi M.
N1 - Publisher Copyright:
© 2019 by the American College of Sports Medicine.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 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. A clear, valid insight into 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 the prediction of symptom resolution time (within 7, 14, or 28 d), with a data set representing 3 yr 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 ANOVA revealed statistically significant performance differences across the 10 classification models for all learners at a 95% confidence interval (P = 0.000). Naïve Bayes and Random Forest with either 100 or 500 trees were the top-performing learners with an area under the receiver operating characteristic curve performance ranging between 0.656 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.
AB - 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. A clear, valid insight into 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 the prediction of symptom resolution time (within 7, 14, or 28 d), with a data set representing 3 yr 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 ANOVA revealed statistically significant performance differences across the 10 classification models for all learners at a 95% confidence interval (P = 0.000). Naïve Bayes and Random Forest with either 100 or 500 trees were the top-performing learners with an area under the receiver operating characteristic curve performance ranging between 0.656 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.
KW - ADOLESCENT
KW - AUGMENTED INTELLIGENCE
KW - RECOVERY
KW - SPORTS MEDICINE
KW - TRAUMATIC BRAIN INJURY
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U2 - 10.1249/MSS.0000000000001903
DO - 10.1249/MSS.0000000000001903
M3 - Article
C2 - 30694980
AN - SCOPUS:85066960275
SN - 0195-9131
VL - 51
SP - 1362
EP - 1371
JO - Medicine and Science in Sports and Exercise
JF - Medicine and Science in Sports and Exercise
IS - 7
ER -