Machine Learning in Modeling High School Sport Concussion Symptom Resolve.
Supplemental digital content is available in the text. 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 r...
| Publicado en: | Medicine & Science in Sports & Exercise Vol. 51; no. 7; pp. 1362 - 1372 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Jul2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136986360&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136986360 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01959131 4DP jtl: Medicine & Science in Sports & Exercise issn: 01959131 maglogo: N pubinfo: dt: Jul2019 vid: 51 iid: 7 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 136986360 136986360 136986360 10.1249/MSS.0000000000001903 136986360 ppf: 1362 ppct: 10 formats: tig: atl: Machine Learning in Modeling High School Sport Concussion Symptom Resolve. aug: au: BERGERON, MICHAEL F. LANDSET, SARA KHOSHGOFTAAR, TAGHI M. MAUGANS, TODD A. WILLIAMS, VERNON B. COLLINS, CHRISTY L. WASSERMAN, ERIN B. affil: SIVOTEC Analytics, Boca Raton, FL sug: subj: Machine Learning Methods Football Injuries Rehabilitation Brain Concussion Rehabilitation Recovery Evaluation Time Factors Students, High School Human Algorithms Incidence Prevalence Headache Dizziness One-Way Analysis of Variance Confidence Intervals ROC Curve Decision Support Systems, Clinical ab: Supplemental digital content is available in the text. 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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