A Machine Learning Approach to Concussion Risk Estimation Among Players Exhibiting Visible Signs in Professional Hockey,Sports Medicine

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A Machine Learning Approach to Concussion Risk Estimation Among Players Exhibiting Visible Signs in Professional Hockey
Sports Medicine ( IF 9.3 ) Pub Date : 2024-09-17 , DOI: 10.1007/s40279-024-02112-2
Jared M Bruce _{1,

2,

3} , Kaitlin E Riegler ₄ , Willem Meeuwisse ₅ , Paul Comper _{6,

7} , Michael G Hutchison ₆ , J Scott Delaney _{8,

9} , Ruben J Echemendia ₁₀

Affiliation

Background

The identification of concussion risk factors, such as visible signs and mechanisms of injury, improves concussion identification. Exploring individual risk factors, such as concussion history, may help to improve existing concussion risk models and algorithms.

Objectives

The primary aim of the current study was to use machine learning techniques to develop a comprehensive, prospectively coded concussion risk model in professional hockey among players exhibiting visible signs. The secondary aim was to examine whether including concussion history improves model performance.

Methods

Data from the National Hockey League (NHL) spotter program, including coded visible signs and mechanisms of injury associated with possible concussive events, were extracted from the 2018–2019 to the 2021–2022 seasons. Each unique spotter event was matched with data extracted from the medical record to determine whether the event was associated with a subsequent physician diagnosed concussion. We compared the ability of three machine learning-based approaches to identify the likelihood of physician diagnosed concussion: conditional inference tree, conditional inference random forest, and logistic regression.

Results

A total of 1563 unique events with visible signs were identified by spotters (183 leading to a concussion diagnosis). A randomly selected training sample had 1250 events (146 concussions) and the remaining set-aside test sample had 313 events (37 concussions). The obtained models performed at a high level with large effects in the training [area under the receiver operating characteristic curve (AUC) = 0.79] and set-aside test data (AUC = 0.82). Concussion history was retained in the tree and logistic regression models, with each additional prior concussion associated with a 1.32 times increased odds of concussion diagnosis.

Conclusions

We present simple tree and logistic algorithms for concussion screening and as diagnostic aids. Our results show that player concussion history can explain additional risk above and beyond that explained by visible signs and mechanisms of injury alone.

中文翻译：

职业曲棍球比赛中出现明显迹象的球员脑震荡风险评估的机器学习方法

背景

识别脑震荡危险因素，例如可见的损伤迹象和机制，可以改善脑震荡的识别。探索个体风险因素，例如脑震荡历史，可能有助于改进现有的脑震荡风险模型和算法。

目标

当前研究的主要目的是利用机器学习技术，在职业曲棍球运动员表现出明显迹象的情况下，开发一个全面的、前瞻性编码的脑震荡风险模型。第二个目的是检查包含脑震荡历史是否可以提高模型性能。

方法

来自国家冰球联盟 (NHL) 观察员计划的数据，包括 2018-2019 年至 2021-2022 赛季的编码可见迹象和与可能的脑震荡事件相关的受伤机制。每个独特的观察员事件都与从医疗记录中提取的数据进行匹配，以确定该事件是否与随后医生诊断的脑震荡相关。我们比较了三种基于机器学习的方法来识别医生诊断脑震荡的可能性的能力：条件推理树、条件推理随机森林和逻辑回归。

结果

观察员总共发现了 1563 起具有明显迹象的独特事件（其中 183 起导致脑震荡诊断）。随机选择的训练样本有 1250 个事件（146 次脑震荡），剩余的预留测试样本有 313 个事件（37 次脑震荡）。所获得的模型在训练[受试者工作特征曲线下面积 (AUC) = 0.79] 和预留测试数据 (AUC = 0.82) 中表现出高水平且效果显着。脑震荡历史保留在树和逻辑回归模型中，每增加一次脑震荡，脑震荡诊断的几率就会增加 1.32 倍。