用健康的社会决定因素来预测认知衰退的预测模型
Yingnan He1, Yu Leng1, Ana-Maria Vranceanu2,3
1Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA.
JAR life
|January 19, 2026
概括
在老年人群中预测认知能力下降风险是可以使用可访问的关于健康社会决定因素的调查数据. 这种方法有助于早期诊断阿尔茨海默氏症和相关痴呆症,特别是在资源不足的环境中.
科学领域:
- 老年学是一门学科.
- 公共卫生 公共卫生
- 计算神经科学是一种神经科学.
背景情况:
- 早期诊断阿尔茨海默病和相关痴呆症 (AD/ADRD) 是至关重要的,但受到生物标志物获取有限的阻碍,特别是在资源较低的地区.
- 需要可访问的方法来识别有认知能力下降风险的个体.
研究的目的:
- 开发和验证一种机器学习模型,以使用易于获得的调查数据来预测认知衰退.
- 确保模型在不同人群中具有可解释性和公平性.
主要方法:
- 使用墨西哥健康和衰老研究 (MHAS) (N=4095) 的纵向数据开发了一种堆叠的整体机器学习模型.
- 该模型利用了2003-2012年的人口,生活方式和健康的社会决定因素 (SDoH) 变量.
- 一个Ridge回归元模型结合了来自Random Forest,LightGBM,XGBoost,Lasso和K-Nearest Neighbors基模型的预测.
主要成果:
- 预测模型在一组持久测试中实现了39.25 (10.2%的认知分数范围) 的根平均平方误差 (RMSE).
- 关键预测因素包括教育,年龄,阅读习惯,生活条件,社会参与和父母教育.
- 公平性分析表明,在代表性不足的子组中存在潜在的偏见,例如那些拥有7-9年教育的人.
结论:
- 可访问,低成本的SDoH调查数据可以有效地预测老年人群的认知衰退风险.
- 将公平性评估整合到预测建模中,对于在不同群体中公平应用至关重要.
- 这种方法支持早期发现AD/ADRD,特别是在资源有限的环境中.
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