在社区中居住的中年和老年人中开发健康老龄化的预测模型:使用机器学习的纵向研究
Daniel E C Leme1, Adriane R Costodio2, Cesar de Oliveira3
1School of Public Health Sciences, University of Waterloo, Waterloo, Ontario, Canada.
Journal of the American Medical Directors Association
|September 13, 2025
概括
机器学习模型可以预测老年人的健康衰老. 关键预测因素包括身体表现和财富,而不活动和肥胖会对健康老龄化结果产生负面影响.
科学领域:
- 老年学是一门学科.
- 公共卫生 公共卫生
- 计算生物学 计算生物学
背景情况:
- 健康老龄化是一个关键的公共卫生问题,具有未定义的决定因素.
- 预测健康老龄化对于制定有针对性的干预措施至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测社区中老年人和老年人的健康衰老.
- 确定影响健康衰老结果的关键特征.
主要方法:
- 一项回顾性队列研究,使用来自英国长度老化研究 (50岁以上的参与者) 的数据.
- 利用了社会人口统计,健康,生活方式和心理社会数据.
- 开发了ML模型 (决策树,逻辑回归,神经网络,随机森林) 并使用了SHapley添加式扩展用于预测分析.
主要成果:
- 随机森林ML模型显示出最好的预测性能 (AUC = 0.78).
- 健康衰老的积极预测因素包括正常的身体表现,更高的家庭财富和更年轻的自我感知年龄 (50-59岁).
- 负面预测因素包括身体不活动,腹部肥胖和缺乏互联网/电子邮件使用.
结论:
- 基于基线特征,ML模型有效地预测了老年人的健康衰老.
- 这些发现可以为公共卫生战略提供信息,以促进积极健康的老龄化.
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