在老年人中预测认知障碍风险:基于机器学习的比较研究和模型开发
Jianwei Li1, Jie Li2, Huafang Zhu3
1Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, China.
Dementia and geriatric cognitive disorders
|May 22, 2024
概括
在老年人中早期发现认知能力下降至关重要. 使用机器学习的新预测模型可以在三年内识别具有较高认知障碍风险的个体.
科学领域:
- 老年学与公共卫生
- 医疗保健中的人工智能
- 认知神经科学 认知神经科学
背景情况:
- 在老年人群中,认知障碍和痴呆的患病率正在上升.
- 早期检测对于及时干预和管理至关重要.
- 识别有风险的个体有助于主动的医疗保健策略.
研究的目的:
- 开发和验证用于早期检测认知衰退的机器学习模型.
- 确定老年人认知障碍的关键预测因素.
- 为社区环境中的初级医疗保健工作人员提供一个工具.
主要方法:
- 利用了2,288名认知正常的老年人的数据.
- 采用递归特征消除和六个机器学习算法.
- 使用AUC,特异性,灵敏度和精度评估模型性能.
- 应用了夏普利添加式解释 (SHAP) 来实现模型的可解释性.
主要成果:
- 纯粹的贝叶斯模型在测试组中实现了0.820的AUC.
- 最重要的预测因素包括基线认知功能 (MMSE),教育,睡眠质量 (PSQI) 和心血管因素.
- 该模型在3年内确定了具有较高认知障碍风险的个体.
结论:
- 一个经过验证的机器学习模型可以帮助早期发现认知障碍.
- 该模型为社区环境中的初级医疗保健提供者提供了一个实用的工具.
- 这种方法支持对老年人群认知能力下降的积极管理.
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