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评估口语作为自动查认知障碍的生物标志物

Maria R Lima^1,2, Alexander Capstick^3,4, Fatemeh Geranmayeh^4,5

¹Department of Mechanical Engineering, Imperial College London, London, UK. maria.lima@imperial.ac.uk.

Communications medicine

|December 13, 2025

概括

使用语音生物标志物的可解释机器学习模型显示出早期阿尔茨海默病和相关痴呆症 (ADRD) 检测和严重性预测的前景. 这种方法可以帮助认知健康监测和临床分拣.

科学领域:

计算语言学计算语言学
神经科学是一个神经科学.
人工智能的人工智能

背景情况:

认知障碍评估是一个关键的未满足的需求.
语音生物标志物为自动查提供了一个可扩展的,非侵入性的方法.
机器学习 (ML) 的临床实用性受到解释性和概括性的限制.

研究的目的:

评估阿尔茨海默病和相关痴呆症 (ADRD) 查和严重性预测的可解释的ML.
在居住数据上验证模型的概括性.
通过风险分层和语言特征重要性评估来提高临床效用.

主要方法:

使用了基准 DementiaBank 语音数据 (N=291) 和飞行员居住数据 (N=22).
采用随机森林模型用于ADRD检测和迷你心理状态考试 (MMSE) 评分预测.
评估语言特征,包括代名词/副词的使用,不流利,分析思维,词汇多样性和心理状态词语.

主要成果:

在ADRD检测模型中,基准数据的灵敏度为69.4%,特异性为83.3%,试验数据的灵敏度为70.0%,特异性为52.5%.
在MMSE得分预测模型中,基准数据的平均绝对误差为3.7,试验数据的平均误差为3.3.
风险分层提高了13%的特异性,识别了与ADRD相关的语言特征.

结论:

预测建模显示了与家庭对话技术集成的潜力,用于认知健康监测.
早期查和干预可以通过对高风险个体进行分类来促进.
可解释的ML增强了认知障碍语音生物标志物的临床实用性.

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