基于机器学习的社区老年人的脆弱性预测:系统性审查和元分析
Yifan Ou1, Dandan Jiang2, Pan Li1
1School of Nursing, University of South China, Hengyang, Hunan, China.
Frontiers in public health
|January 28, 2026
概括
机器学习模型预测老年人的脆弱性,但性能各不相同. 这次审查发现AUC的AUC从0.730到0.878不等,强调了需要更高质量的适用预测模型的需求.
科学领域:
- 老年学是一门学科.
- 人工智能的人工智能
- 生物统计学 生物统计学
背景情况:
- 使用机器学习 (ML) 方法开发越来越多的社区老年人的脆弱性预测模型.
- 模型性能的变化阻碍了它们的实际应用.
- 这项研究系统地审查和评估基于ML的脆弱性预测模型.
研究的目的:
- 总结和评估基于ML的脆弱性预测模型的性能.
- 评估这些模型的临床适用性.
- 在未来的模型开发中确定需要改进的领域.
主要方法:
- 进行了系统性审查和元分析.
- 进行广泛的数据库搜索 (PubMed,科学网,Embase等) 在2025年6月10日之前进行.
- 使用PROBAST+AI工具评估研究质量,并使用Stata18.0.0.进行曲线下面积 (AUC) 的元分析.
主要成果:
- 包含了10项使用45个ML模型的研究 (36个内部,9个外部验证).
- 基线脆弱性预测的AUC是0.878 (内部验证).
- 纵向脆弱性预测的聚合AUC为0.730 (内部验证),整体聚合AUC为0.786.
结论:
- 大多数模型都表现出良好的区分和校准.
- 然而,目前研究的整体质量和适用性仍然存在问题.
- 未来的研究应该遵循TRIPOD+AI和PROBAST+AI的指导方针,以获得更高质量的,更适用的预测模型.
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