基于社会生态预测器的事件可逆认知脆弱性预测模型的开发和验证,使用通用线性混合模型和机器学习算法:前性队列研究
Qinqin Liu1, Huaxin Si2, Yanyan Li1
1School of Nursing, Peking University, Beijing, China.
概括
这项研究开发了老年人可逆认知虚弱 (RCF) 的预测模型. 通用线性混合模型 (GLMM) 在早期RCF识别方面表现最好.
科学领域:
- 老年学是一门学科.
- 认知科学 认知科学
- 公共卫生 公共卫生
背景情况:
- 可逆性认知衰弱 (RCF) 对全球老龄化人口构成重大挑战.
- 早期识别和干预对于管理RCF和维持老年人的认知健康至关重要.
- 社会生态因素在认知衰退的发展中起着复杂的作用.
研究的目的:
- 开发和验证使用社会生态预测器对事件可逆认知脆弱性 (RCF) 的预测模型.
- 为了比较各种机器学习模型在预测RCF方面的性能.
- 为早期干预策略确定高风险人群.
主要方法:
- 利用了2011-2013年中国健康与退休长度研究 (CHARLS) (培训组,n=1230) 和2013-2015年 (外部验证组,n=1631) 的数据.
- 开发了使用通用线性混合模型 (GLMM),极端梯度提升,支持向量机,随机森林和二元混合模型森林的预测模型.
- 通过5倍交叉验证和外部验证评估模型,评估歧视 (AUC) 和校准.
主要成果:
- 在训练组中,GLMM表现出良好的区别 (AUC=0.765).
- 所有模型在内部和外部验证期间都表现出公平的歧视 (AUC=0.578-0.667).
- 模型显示了可接受的校准,整体预测性能和在训练和验证数据集中的临床有用性.
- 基于GLMM的风险评分有效地将老年人分为三个RCF风险组.
结论:
- 该GLMM提供了一个有价值的工具,用于预测事件RCF在老年人.
- 基于GLMM预测的风险分层可以帮助医疗保健提供者在早期识别高风险的个人RCF.
- 这些发现支持开发有针对性的干预措施,以减轻RCF的进展.
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