在美国,使用倾向性得分匹配和机器学习进行认知衰退风险的多维分析和预测建模
Jiaxin Li1, Guozhen Chen2, Yuanchu Liu2
1Multifunctional Electronic Ceramics Laboratory, College of Engineering, Xi'an International University, Xi'an, China.
Medicine
|January 24, 2026
概括
高风险状态显著导致认知能力下降,在美国各地有显著的地理差异. 机器学习模型有效地识别了有针对性的公共卫生干预的风险人群.
科学领域:
- 公共卫生 公共卫生
- 神经科学是一个神经科学.
- 数据科学数据科学数据科学
背景情况:
- 认知能力下降是神经退行性疾病的早期指标,也是越来越严重的公共卫生问题.
- 了解高风险状态对认知功能的因果影响对于早期干预至关重要.
- 认知衰退的地理和时间变化需要本地化的公共卫生策略.
研究的目的:
- 用因果推理方法量化高风险状态对认知功能的因果影响.
- 探索高风险状态和认知衰退之间的关系的地理和时间异质性.
- 开发和评估机器学习模型,以早期识别有认知能力下降风险的个体.
主要方法:
- 分析了来自行为风险因素监测系统 (BRFSS) 的22,182个记录.
- 使用倾向性得分匹配和双重可靠估计来解决混的因果效应的估计.
- 使用80/20培训/测试分割开发和评估预测模型 (随机森林,XGBoost).
主要成果:
- 证实了高风险状态和认知衰退之间存在显著的因果关系,平均治疗效果 (ATE) 为19.92 (PSM) 和16.95 (DRE).
- 观察到明显的区域异质性,美国领土呈现最高的ATE (59.40).
- 随机森林模型显示出卓越的预测性能 (准确度:0.77,F1:0.67),有效地平衡了精度和回忆.
结论:
- 高风险状态因果关系有助于认知能力下降,在美国存在巨大的地理差异.
- 综合因果推断和机器学习为了解风险因素和开发选工具提供了强大的框架.
- 数据驱动的洞察力可以指导公共卫生干预,并增强认知健康管理策略.
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