S'abonner

Machine learning to predict sports-related concussion recovery using clinical data - 05/08/22

Doi : 10.1016/j.rehab.2021.101626 
Yan Chu a, Gregory Knell b, c, d, Riley P. Brayton b, c, d, Scott O. Burkhart d, Xiaoqian Jiang a, Shayan Shams a, e,
a School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA 
b Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston (UTHealth), Dallas, TX, USA 
c Children's Health and The University of Texas Health Science Center at Houston (UTHealth), Dallas, TX, USA 
d Children's Health Andrews Institute for Orthopaedics and Sports Medicine, Plano, TX, USA 
e Department of Applied Data Science, San Jose State University, San Jose, CA, USA 

Corresponding author at: Department of Applied Data Science, San Jose State University, One Washington Sq, San Jose, CA 95192.Department of Applied Data ScienceSan Jose State UniversityOne Washington SqSan JoseCA95192

Bienvenue sur EM-consulte, la référence des professionnels de santé.
Article gratuit.

Connectez-vous pour en bénéficier!

Highlights

Machine learning was used to identify protracted concussion recovery in adolescents.
CatBoost outperformed other machine learning models in recovery time prediction.
CatBoost had a better discriminatory ability than a human-driven statistical model.
Variables significant for classification accuracy differed between males and females.

Le texte complet de cet article est disponible en PDF.

Abstract

Objectives

Sport-related concussions (SRCs) are a concern for high school athletes. Understanding factors contributing to SRC recovery time may improve clinical management. However, the complexity of the many clinical measures of concussion data precludes many traditional methods. This study aimed to answer the question, what is the utility of modeling clinical concussion data using machine-learning algorithms for predicting SRC recovery time and protracted recovery?

Methods

This was a retrospective case series of participants aged 8 to 18 years with a diagnosis of SRC. A 6-part measure was administered to assess pre-injury risk factors, initial injury severity, and post-concussion symptoms, including the Vestibular Ocular Motor Screening (VOMS) measure, King-Devick Test and C3 Logix Trails Test data. These measures were used to predict recovery time (days from injury to full medical clearance) and binary protracted recovery (recovery time > 21 days) according to several sex-stratified machine-learning models. The ability of the models to discriminate protracted recovery was compared to a human-driven model according to the area under the receiver operating characteristic curve (AUC).

Results

For 293 males (mean age 14.0 years) and 362 females (mean age 13.7 years), the median (interquartile range) time to recover from an SRC was 26 (18–39) and 21 (14–31) days, respectively. Among 9 machine-learning models trained, the gradient boosting on decision-tree algorithms achieved the best performance to predict recovery time and protracted recovery in males and females. The models’ performance improved when VOMS data were used in conjunction with the King-Devick Test and C3 Logix Trails Test data. For males and females, the AUC was 0.84 and 0.78 versus 0.74 and 0.73, respectively, for statistical models for predicting protracted recovery.

Conclusions

Machine-learning models were able to manage the complexity of the vestibular-ocular motor system data. These results demonstrate the clinical utility of machine-learning models to inform prognostic evaluation for SRC recovery time and protracted recovery.

Le texte complet de cet article est disponible en PDF.

Keywords : Machine learning, Brain concussion, Adolescent, Athletic injuries/rehabilitation, Vestibular function tests, Sport injuries


Plan


© 2022  The Authors. Publié par Elsevier Masson SAS. Tous droits réservés.
Ajouter à ma bibliothèque Retirer de ma bibliothèque Imprimer
Export

    Export citations

  • Fichier

  • Contenu

Vol 65 - N° 4

Article 101626- juin 2022 Retour au numéro
Article précédent Article précédent
  • Isokinetic quadriceps symmetry helps in the decision to return to running after anterior cruciate ligament reconstruction
  • Marc Dauty, Pascal Edouard, Pierre Menu, Olivier Mesland, Alban Fouasson-Chailloux
| Article suivant Article suivant
  • Knee strength symmetry at 4 months is associated with criteria and rates of return to sport after anterior cruciate ligament reconstruction
  • Joffrey Drigny, Clémence Ferrandez, Antoine Gauthier, Henri Guermont, César Praz, Emmanuel Reboursière, Christophe Hulet

Bienvenue sur EM-consulte, la référence des professionnels de santé.

Mon compte


Plateformes Elsevier Masson

Déclaration CNIL

EM-CONSULTE.COM est déclaré à la CNIL, déclaration n° 1286925.

En application de la loi nº78-17 du 6 janvier 1978 relative à l'informatique, aux fichiers et aux libertés, vous disposez des droits d'opposition (art.26 de la loi), d'accès (art.34 à 38 de la loi), et de rectification (art.36 de la loi) des données vous concernant. Ainsi, vous pouvez exiger que soient rectifiées, complétées, clarifiées, mises à jour ou effacées les informations vous concernant qui sont inexactes, incomplètes, équivoques, périmées ou dont la collecte ou l'utilisation ou la conservation est interdite.
Les informations personnelles concernant les visiteurs de notre site, y compris leur identité, sont confidentielles.
Le responsable du site s'engage sur l'honneur à respecter les conditions légales de confidentialité applicables en France et à ne pas divulguer ces informations à des tiers.


Tout le contenu de ce site: Copyright © 2024 Elsevier, ses concédants de licence et ses contributeurs. Tout les droits sont réservés, y compris ceux relatifs à l'exploration de textes et de données, a la formation en IA et aux technologies similaires. Pour tout contenu en libre accès, les conditions de licence Creative Commons s'appliquent.