期刊文献+

基于机器学习的运动损伤预警模型

Sports injury prediction model based on machine learning
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摘要 背景:运动医学界广泛呼吁采用机器学习技术高效处理庞大、冗杂的运动数据资源,构建智能化的运动损伤预警模型,以实现运动损伤的精准预警。对此类研究成果进行综合归纳与评述,对把握预警模型改进方向,指导中国损伤预警模型构建工作均具有重要意义。目的:系统梳理基于机器学习技术的运动损伤预警模型相关研究,为中国运动损伤预警模型构建工作提供借鉴。方法:对中国知网、Web of Science和EBSCO数据库进行文献检索,主要检索机器学习技术和运动损伤相关文献,最终纳入61篇运动损伤预警模型相关文献进行分析。结果与结论:①在纳入文献的外部风险特征指标中,缺乏比赛场景类指标,后续需进一步完善相关特征指标的纳入工作,以进一步丰富模型训练的数据集维度;此外,运动损伤预警模型的纳入特征权重方法以过滤法为主,需强化嵌入法及包裹法等权重方法的运用,以增强多风险因素交互效应的分析。②在模型主体训练方面,模型主体训练算法多以监督式学习算法为主,此类算法对样本标注信息的完整度有较高要求,应用场景易受限,后期可增加无监督式与半监督式算法的应用。③在模型性能评估优化方面,现研究主要采用了HoldOut交叉与k-交叉两种验证方式评估模型性能,模型的AUC值范围(0.76±0.12),灵敏度范围(75.92±11.03)%,特异度范围(80.03±4.54)%,F1分数值范围(80.60±10.63)%,准确度范围(69.96±13.10)%,精确度范围(70±14.71)%,数据增强与特征优化为最常见的模型优化操作。当前运动损伤预警模型准确度及精确度均约为70%,预警效果良好,但模型优化操作较单一,多采用数据增强方法提升模型性能,需强化对模型算法、超参数的调整,以进一步提升模型性能。④在模型特征提取方面,纳入的内部风险特征指标多以人体测量学、训练负荷、训练年限和损伤史等指标为主, BACKGROUND:The sports medicine community has widely called for the use of machine learning technology to efficiently process the huge and complicated sports data resources,and construct intelligent sports injury prediction models,enabling accurate early warning of sports injuries.It is of great significance to comprehensively summarize and review such research results so as to grasp the direction of early warning model improvement and to guide the construction of sports injury prediction models in China. OBJECTIVE: To systematically review and analyze relevant research on sports injury prediction models based on machine learning technology, thereby providing references for the development of sports injury prediction models in China. METHODS: Literature search was conducted on CNKI, Web of Science and EBSCO databases, which mainly searched for literature related to machine learning techniques and sports injuries. Finally, 61 articles related to sports injury prediction models were included for analysis. RESULTS AND CONCLUSION: (1) In terms of external risk feature indicators, there is a lack of competition scenario indicators, and the inclusion of related feature indicators needs to be further improved to further enrich the dimensions of the dataset for model training. In addition, the inclusion feature weighting methods of the sports injury prediction model are mainly based on filtering methods and the use of embedding and wrapping weighting methods needs to be strengthened in order to enhance the analysis of the interaction effects of multiple risk factors. (2) In terms of model body training, supervised learning algorithms become the mainstream choice. Such algorithms have higher requirements for the completeness of sample labeling information, and the application scenarios are easily limited. Therefore, the application of unsupervised and semi-supervised algorithms can be increased in the later stage. (3) In terms of model performance evaluation and optimization, the current studies mainly adopt two verificati
作者 魏梦力 钟亚平 桂辉贤 周易文 关烨明 余绍华 Wei Mengli;Zhong Yaping;Gui Huixian;Zhou Yiwen;Guan Yeming;Yu Shaohua(Sports Big Data Research Center of Wuhan Sports University,Wuhan 430079,Hubei Province,China;Hubei Sports and Health Innovation and Development Research Center,Wuhan 430079,Hubei Province,China)
出处 《中国组织工程研究》 CAS 北大核心 2025年第2期409-418,共10页 Chinese Journal of Tissue Engineering Research
基金 国家社科基金后期资助重点项目(22FTYA001) 国家体育总局决策咨询研究项目(2023-B-19) 湖北省高等学校省级教学研究项目(2022395)。
关键词 运动损伤 损伤预警 损伤预防 智能预警 机器学习 深度学习 人工智能 体育运动 sports injury injury warning injury prevention intelligent warning machine learning deep learning artificial intelligence sports
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