通过语言的镜头在阿尔茨海默病的情节性思维:语言分析和基于变压器的分类
Rui He1, Xiaofeng Yuan1, Wolfram Hinzen1,2
1Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
American journal of speech-language pathology
|October 23, 2023
概括
在阿尔茨海默病 (AD) 中,情节性记忆衰退在言语中是可检测的. 语言中这种偶发性损失是阿尔茨海默病的早期指标,可由人类和机器测量.
科学领域:
- 神经科学是一个神经科学.
- 语言学的语言学.
- 人工智能的人工智能
背景情况:
- 情节性记忆衰退是阿尔茨海默病 (AD) 的一个关键特征.
- 这种下降与面向未来的情节性思维受损有关.
- 在自发言语中检测这些缺陷的潜力仍然未被充分探索.
研究的目的:
- 调查是否可以直接从连接的语言中识别出情节性思维的缺陷.
- 探索在阿尔茨海默病中自动检测情节性思维缺陷的可行性.
主要方法:
- 从70名健康对照组,82名轻度可能的AD (pAD) 和46名中度的pAD患者的2809个发言的语言分类.
- 使用通用线性回归模型分析情节性,非情节性和"其他"发言类别.
- 应用深度学习 (基于变压器) 分类器来自动化情节性分析.
主要成果:
- 与对照人群相比,在轻度和中度AD患者中观察到显著的发作性下降.
- 插曲性下降与认知能力相关,而不是年龄,性别或教育程度.
- 自动分类器在三元分类中获得了0.913的宏F1得分,这表明其准确度很高.
结论:
- 节奏性丧失是阿尔茨海默病在自发言语中的早期表现.
- 这种语言缺陷可以通过人类分析和自动机器学习方法可靠地检测到.
- 语音分析为早期AD检测和监测提供了一个有希望的途径.
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