使用机器学习对与跑步相关的伤害进行多学科预测
Han Wu1, Katherine Brooke-Wavell2, Michael R Barnes3
1School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK. h.wu4@lboro.ac.uk.
NPJ digital medicine
|February 6, 2026
概括
这项研究开发了一套机器学习数据集,用于使用各种风险因素预测耐力跑伤害. 这些模型在伤害预测准确度上显示了适度的改进,随机森林表现最好.
科学领域:
- 运动医学 运动医学
- 生物机械工程 生物机械工程
- 数据科学数据科学数据科学
背景情况:
- 耐力跑步相关的伤害 (RRI) 有复杂的,多因素的原因.
- 现有的研究往往忽视了个性化RRI预测的多学科风险因素.
研究的目的:
- 为每周的RRI预测创建一个机器学习准备的数据集.
- 通过使用多学科风险因素来评估机器学习模型的有效性.
主要方法:
- 在12个月内从142名竞争性跑步者的遗传,历史,生物力学,生理和训练因素中收集了数据.
- 开发和测试机器学习模型,使用高证据和更广泛的风险因素.
- 对RRI进行前性监测,每周收集6181个样本.
主要成果:
- 机器学习模型的AUC达到0.784±0.014,比之前的RRI预测有所改善.
- 随机森林模型显示了最高的性能 (AUC = 0.781 ± 0.016).
- 随着更广泛的风险因素,物流回归性能显著改善.
结论:
- 引入了基于机器学习的体育伤害预测的可重复框架.
- 为未来的大规模体育伤害分析提供了有价值的数据集.
- 强调了多学科数据和模型选择对于准确的RRI预测的重要性.
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