在老年人中检测认知障碍和心理健康
Xiaofan Mu1, Merna Bibars2,3, Salman Seyedi4
1Department of Computer Science, Emory University, Atlanta, GA 30322, United States of America.
概括
机器学习模型可以使用面部,声学和语言数据监测老年人的认知衰退和心理健康. 进一步的研究需要大量的数据集,以便为各种人群创建可泛化的模型.
科学领域:
- 老年学是指老年学的学科.
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 人口老龄化需要可扩展的方法来监测认知衰退和识别痴呆症风险因素.
- 社会和心理健康是老年人认知健康的关键方面.
研究的目的:
- 开发和评估机器学习模型来量化认知状态,社会隔离和老年人的心理健康.
- 识别关键特征 (面部,声学,语言,心血管) 用于监测认知和心理健康.
主要方法:
- 机器学习模型训练了39名老年人的面部,声学,语言和心血管特征 (正常认知或轻度认知障碍).
- 数据是通过远程视频对话收集的.
- 模型量化了认知状态 (临床痴呆症评分表),社会隔离,社会满意度,心理健康和负面影响.
主要成果:
- 该模型在区分认知障碍和量化社会/心理因素方面取得了很高的准确性 (AUC 0.72-0.77).
- 语音和语言模式是认知障碍的关键,而面部和心血管模式对社会/心理健康很重要.
- 偏差分析揭示了心理健康和认知状态模型中显著的年龄,性别,疾病和与教育有关的偏差.
结论:
- 使用机器学习监测老年人的认知和心理健康是可行的.
- 大规模,多样化的数据集对于为老年人开发可概括的深度学习模型至关重要.
- 解决与人口统计和疾病相关的偏见对于老年学中公平的AI应用至关重要.
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