临床表现 临床表现
Yignan He1, Yu Leng1, Ana-Maria Vranceanu1
1Massachusetts General Hospital, Boston, MA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
概括
健康的社会决定因素 (SDoH) 数据可以预测认知衰退,为早期发现认知障碍提供了一种可访问的方法. 这项研究开发了一个强大的堆叠组合模型,使用SDoH改进痴呆症查.
科学领域:
- 老年学是一门学科.
- 公共卫生 公共卫生
- 机器学习 机器学习
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,但目前的方法面临着可访问性挑战.
- 健康的社会决定因素 (SDoH) 和生活方式因素为预测认知衰退提供了可访问的替代方案.
研究的目的:
- 使用SDoH数据开发和验证用于早期检测认知障碍的堆叠组合模型.
- 评估基于SDoH的认知衰退预测的可行性和性能.
主要方法:
- 利用了来自墨西哥健康与衰老研究 (MHAS) 的数据,这是一项对50岁以上成年人进行的纵向调查.
- 采用双层堆叠组合架构,使用人口,经济,健康和生活方式变量预测全球认知.
- 在多年来收集的数据上训练并测试了模型,以预测认知功能.
主要成果:
- 整体模型实现了39.25的根-平均-平方偏差 (RMSE),表明大约10.2%的偏差与完整的认知得分范围.
- 轻GBM是表现最好的基本模型,功能重要性分析确定了关键的社会和生活方式因素.
- 敏感性分析证实了模型的稳定性,尽管在某些子组中由于数据集表示而存在偏差.
结论:
- SDoH数据为早期发现认知障碍提供了一种可行的方法,平衡预测准确性和可解释性.
- 开发的模型展示了可访问数据对认知健康评估的潜力.
- 未来的研究应该专注于预测认知得分的变化,考虑到SDoH的影响.
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