机器学习对骨质疏松骨折风险的预测价值:系统性审查和元分析
Yanqian Wu1, Jianqian Chao2, Min Bao1
1Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China.
BMJ open
|December 9, 2023
概括
机器学习 (ML) 显示出预测骨质疏松症患者骨折风险的前景,其综合C指数为0.75. 需要进一步的外部验证来确认这些ML模型在临床使用中的可靠性.
科学领域:
- 医疗信息学 医疗信息学
- 整形外科 整形外科 整形外科
- 老年学是一门学科.
背景情况:
- 早期识别骨折风险对于治疗骨质疏松症至关重要.
- 机器学习 (ML) 为骨折风险预测提供了潜力,但其有效性仍在争论中.
- 这项研究系统地审查和分析了ML在骨质疏松症中的预测性能.
研究的目的:
- 评估机器学习模型对骨质疏松症患者骨折风险的预测效率.
- 利用元分析方法对ML的性能进行综合现有证据.
主要方法:
- 来自四个主要数据库的2023年5月31日之前发表的研究的系统审查和元分析.
- 使用随机效应和双变异混合效应模型对C指数,灵敏度和特异性的元分析.
- 根据ML模型类型和骨折部位进行了子组分析.
主要成果:
- 包括53项研究,涉及超过1500万名患者和86个ML预测模型.
- 常见的预测因素包括年龄,BMI,骨折史,骨矿物质密度 (BMD) 和跌倒.
- 在训练和验证组中,聚合的C指数为0.75;聚合的灵敏度和特异性也很好.
结论:
- 机器学习证明了骨质疏松症中骨折风险的良好预测性能.
- 一个显著的局限性是大多数当前研究中缺乏外部验证.
- 外部验证对于确认临床应用ML模型的可靠性和通用性至关重要.
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