使用贝叶斯网络来根据足球伤病流行病学数据对回归体育时间进行分类
Kate K Y Yung1,2,3,4, Paul P Y Wu2,3, Karen Aus der Fünten4
1Department of Orthopaedics and Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong.
PloS one
|March 20, 2025
概括
这项研究引入了贝叶斯网络 (BN) 来预测职业足球运动员的回归体育 (RTS) 时间表和受伤严重程度. 该BN整合了球员,比赛和伤害数据,以帮助临床决策和团队规划.
科学领域:
- 运动医学 运动医学
- 数据科学数据科学数据科学
- 流行病学 流行病学
背景情况:
- 回归体育 (RTS) 预测是复杂的,受到内在和外在因素的影响.
- 个性化RTS时间估计具有挑战性,临床决策支持工具有限.
- 职业足球 (德甲) 数据为伤害分析提供了丰富的来源.
研究的目的:
- 开发和验证贝叶斯网络 (BN) 以预测回归运动时间 (RTS) 和受伤严重程度.
- 将临床,非临床因素和专家知识整合到预测模型中.
- 提供一个工具,帮助教练和球员在RTS决策和场景评估.
主要方法:
- 利用了3374个球员赛季和6143次德国德甲 (2014-2021) 伤病的回顾性伤病数据.
- 应用了贝叶斯网络 (BN),包含12个变量 (球员特征,比赛和受伤信息).
- 模拟了两个响应变量:到RTS的几天和损伤严重程度 (最小,轻度,中度,严重).
主要成果:
- 该BN显示RTS日 (0.24-0.97) 和严重程度 (0.73-1.00) 的敏感性.
- 用户对BN的准确性达到RTS天的0.52-0.83和严重程度的0.67-1.00.
- 该模型有效地整合了各种数据类型,以预测损伤结果.
结论:
- 贝叶斯网络可以有效地整合各种数据类型来预测结果,例如回归体育的时间表.
- 开发的BN可以帮助预测RTS日,促进团队规划和了解受伤风险因素.
- 这项研究强调了贝叶斯网络在RTS运动医学中增强临床决策的潜力.
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