Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes.
Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes.
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用于预测美国职业篮球联赛（NBA）运动员下肢肌肉拉伤的机器学习方法
DOI：
10.1177/23259671221111742
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发表时间：
2022-07
影响因子：
2.6
通讯作者：
Camp, Christopher L.
中科院分区：
文献类型：
--
作者：
Lu, Yining;Pareek, Ayoosh;Lavoie-Gagne, Ophelie Z.;Forlenza, Enrico M.;Patel, Bhavik H.;Reinholz, Anna K.;Forsythe, Brian;Camp, Christopher L.
在职业体育领域，导致运动员缺席比赛时间的伤病，对运动员个人和所属机构都有着重大影响。利用机器学习对受伤概率进行量化的研究重新引发了人们的关注，而有效模型的开发有望辅助队医的决策过程。
本研究旨在：（1）描述1999年至2019年美国职业篮球联赛（NBA）中导致缺席比赛时间的下肢肌肉拉伤（LEMS）的流行病学特征；（2）确定一种机器学习模型在预测受伤风险方面的有效性。研究假设在该群体中导致缺席比赛时间的LEMS并不常见，且机器学习模型在预测受伤风险方面将优于传统方法。
研究类型为病例对照研究；证据等级为3级。
研究收集了1999年至2019年NBA赛季中4种主要肌肉拉伤类型（腘绳肌、股四头肌、小腿和腹股沟）的表现数据及发生率。伤病包括所有公开报道的导致缺席比赛时间的伤病情况。使用随机森林、极端梯度提升（XGBoost）、神经网络、支持向量机、弹性网络惩罚逻辑回归和广义逻辑回归生成预测LEMS发生的模型。通过区分度、校准度、决策曲线分析和布里尔分数来比较模型性能。
在2103名运动员中，共发生了736例导致缺席比赛时间的LEMS。预测LEMS的重要变量包括既往下肢受伤次数、年龄、近期脚踝、腘绳肌或腹股沟受伤史、近期脑震荡史，以及三分球出手率和罚球出手率。基于通过内部验证评估的区分度（受试者工作特征曲线下面积为0.840）、校准度和决策曲线分析，XGBoost模型表现最佳。
在预测导致缺席比赛时间的LEMS方面，诸如XGBoost等机器学习算法优于逻辑回归。多个变量会增加LEMS的风险，包括各种下肢受伤史、近期脑震荡史以及既往受伤总次数。
In professional sports, injuries resulting in loss of playing time have serious implications for both the athlete and the organization. Efforts to quantify injury probability utilizing machine learning have been met with renewed interest, and the development of effective models has the potential to supplement the decision-making process of team physicians. The purpose of this study was to (1) characterize the epidemiology of time-loss lower extremity muscle strains (LEMSs) in the National Basketball Association (NBA) from 1999 to 2019 and (2) determine the validity of a machine-learning model in predicting injury risk. It was hypothesized that time-loss LEMSs would be infrequent in this cohort and that a machine-learning model would outperform conventional methods in the prediction of injury risk. Case-control study; Level of evidence, 3. Performance data and rates of the 4 major muscle strain injury types (hamstring, quadriceps, calf, and groin) were compiled from the 1999 to 2019 NBA seasons. Injuries included all publicly reported injuries that resulted in lost playing time. Models to predict the occurrence of a LEMS were generated using random forest, extreme gradient boosting (XGBoost), neural network, support vector machines, elastic net penalized logistic regression, and generalized logistic regression. Performance was compared utilizing discrimination, calibration, decision curve analysis, and the Brier score. A total of 736 LEMSs resulting in lost playing time occurred among 2103 athletes. Important variables for predicting LEMS included previous number of lower extremity injuries; age; recent history of injuries to the ankle, hamstring, or groin; and recent history of concussion as well as 3-point attempt rate and free throw attempt rate. The XGBoost machine achieved the best performance based on discrimination assessed via internal validation (area under the receiver operating characteristic curve, 0.840), calibration, and decision curve analysis. Machine learning algorithms such as XGBoost outperformed logistic regression in the prediction of a LEMS that will result in lost time. Several variables increased the risk of LEMS, including a history of various lower extremity injuries, recent concussion, and total number of previous injuries.
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