基于自然语言处理的早期阿尔茨海默氏症的分类从连接的语音
Helena Balabin1,2, Bastiaan Tamm1,3, Laure Spruyt1
1Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|January 27, 2025
概括
自传采访显示,作为阿尔茨海默病 (AD) 检测的生物标志物具有前途. 连接语音的自动分析,特别是使用这种方法,可以帮助识别早期的AD患者.
科学领域:
- 神经学 神经学
- 计算语言学 计算语言学
- 生物标志物 生物标志物
背景情况:
- 通过自然语言处理 (NLP) 来自动分析连接的语音,为阿尔茨海默病 (AD) 提供了潜在的生物标志物.
- 确定特定的语音类型是最敏感的,并且对AD检测具有特异性仍然是一个挑战.
研究的目的:
- 评估不同连接式语音类型在区分早期阿尔茨海默病 (AD) 患者中的有效性.
- 评估语言模型在基于粉样蛋白状况的认知无障碍 (CU) 个体分类方面的表现.
主要方法:
- 一个语言模型被应用到自动转录的114个佛兰德语说话的人的连接语音.
- 分析了五种不同类型的连接语音,以区分早期阿尔茨海默病患者和粉素阴性CU个体,以及粉素阴性和粉素阳性CU个体.
主要成果:
- 语言模型在区分阿尔茨海默病患者与粉样阴性CU受试者方面达到81.9%的灵敏度和81.8%的特异性.
- 区分粉素阳性和粉素阴性CU个体的分类准确度高达82.7%的灵敏度和74.0%的特异性.
- 与场景描述相比,自传采访显示出更高的表现.
结论:
- 自传采访对于在AD检测中自动分析连接语音非常有价值.
- 在语音分析之前的临床AD分类可以提高健康个体粉样蛋白状态分类的准确性.
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