使用个人,社会和功能生活方式的方面选轻度认知障碍:机器学习方法
Kyle Masato Ishikawa1, Matthew Uechi2, Hyeong Jun Ahn1
1Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, Hawaii, United States of America.
PloS one
|October 24, 2025
概括
机器学习模型可以使用生活方式因素预测轻度认知障碍 (MCI). 年龄,种族,功能困难,社会脱节和压力是早期MCI识别的关键预测因素.
科学领域:
- 老年学是一门学科.
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 轻度认知障碍 (MCI) 表示认知能力下降超出正常衰老,并增加痴呆风险.
- 早期识别MCI对于及时进行预防性干预至关重要,但许多病例在初级保健机构中仍未被发现.
- 现有的查方法往往缺乏在认知衰退的早期阶段捕捉个体的灵敏度.
研究的目的:
- 评估机器学习 (ML) 模型在预测MCI方面的有效性.
- 利用例行收集的个人,社会和功能生活方式因素作为MCI的预测因素.
- 确定老年人中MCI最重要的预测因素.
主要方法:
- 利用了来自国家社会生活,健康和衰老项目 (NSHAP) 的4,586名老年人的数据,并使用了蒙特利尔认知评估 (MoCA) 评分.
- 训练和调整了8个ML模型,包括后勤回归和集合方法,使用人口统计学,生活方式因素和心理社会措施.
- 使用AUROC,精度,灵敏度,特异性和MCC等指标评估模型性能,其中20%的数据保留用于测试.
主要成果:
- 大多数ML模型表现出良好的预测歧视,AUROC超过0.8.
- 一个堆叠的集合模型实现了最高的性能 (AUROC=0.823),而后勤回归提供了准确性和可解释性的最佳平衡 (AUROC=0.818).
- 在模型中一致识别的关键预测因素包括年龄,种族,功能困难,社会脱节和感知压力.
结论:
- 后勤回归为识别患有MCI风险的个体提供了一个实用和可解释的工具.
- 心理社会和功能因素是MCI早期查的重要指标,能够迅速干预.
- 未来的研究应该包括纵向数据和临床验证,以完善MCI预测模型.
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