The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning (ML) models captured facial, acoustic, linguistic, and cardiovascular features from 39 older adults with normal cognition or Mild Cognitive Impairment (MCI), derived from remote video conversations and quantified their cognitive status, social isolation, neuroticism, and psychological well-being. Our model could distinguish Clinical Dementia Rating Scale (CDR) of 0.5 (vs. 0) with 0.77 area under the receiver operating characteristic curve (AUC), social isolation with 0.74 AUC, social satisfaction with 0.75 AUC, psychological well-being with 0.72 AUC, and negative affect with 0.74 AUC. Our feature importance analysis showed that speech and language patterns were useful for quantifying cognitive impairment, whereas facial expressions and cardiovascular patterns were useful for quantifying social and psychological well-being. Our bias analysis showed that the best-performing models for quantifying psychological well-being and cognitive states in older adults exhibited significant biases concerning their age, sex, disease condition, and education levels. Our comprehensive analysis shows the feasibility of monitoring the cognitive and psychological health of older adults, as well as the need for collecting largescale interview datasets of older adults to benefit from the latest advances in deep learning technologies to develop generalizable models across older adults with diverse demographic backgrounds and disease conditions.

翻译：老龄化社会亟需可扩展的方法来监测认知衰退，并识别老年人中预示痴呆风险的社会与心理因素。我们的机器学习模型从39名认知正常或患有轻度认知障碍的老年人中，通过远程视频对话提取了面部、声学、语言及心血管特征，并量化了其认知状态、社会孤立感、神经质及心理健康水平。模型区分临床痴呆评定量表得分为0.5（对比0）的受试者时，接收者操作特征曲线下面积为0.77；识别社会孤立感的AUC为0.74，社会满意度的AUC为0.75，心理健康的AUC为0.72，负面情绪的AUC为0.74。特征重要性分析表明，语音和语言模式有助于量化认知障碍，而面部表情和心血管模式则对量化社会与心理健康更为有效。偏差分析显示，在量化老年人心理健康与认知状态时表现最佳的模型，在年龄、性别、疾病状况及教育水平方面存在显著偏差。综合分析表明，监测老年人认知与心理健康具有可行性，同时需要收集大规模老年人访谈数据集，以利用深度学习技术的最新进展，开发适用于不同人口背景和疾病状况的泛化模型。