生物标志物 生物标志物
Eloïse DA Cunha1,2,3,4, Raphael Zory5, Frédéric Chorin4,6
1Interdisciplinary Institute of Artificial Intelligence, Université Côte d'Azur, Nice, Alpes Maritimes, France.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
概括
分析语音的机器学习模型可以有效地选老年人的身体虚弱,这是与阿尔茨海默病风险相关的疾病. 这种非侵入性方法为老龄化人口的及时干预提供了可扩展的早期检测.
科学领域:
- 老年学是一门学科.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 老年人的身体虚弱是一种可逆的疾病,与阿尔茨海默病 (AD) 等神经认知障碍的风险增加有关.
- 早期发现脆弱性对于预防性干预至关重要,但非侵入性查方法有限.
- 语音标记有潜力用于神经退行性病理的识别,但它们在脆弱性检测中的作用尚未被探索.
研究的目的:
- 评估机器学习模型使用基于语音的特征,以进行可扩展的,老年人身体虚弱的非侵入性查.
- 确定声学,时间和语言语音特征在分类脆弱状态中的有效性.
主要方法:
- 271名参与者 (≥65岁) 接受了身体和认知评估;使用脆度指数来确定脆弱性.
- 自发的1分钟演讲被录制并分析了声学,时间和语言特征.
- 随机森林,XGBoost和支持矢量机 (SVM) 模型被训练来分类脆弱状态.
主要成果:
- SVM模型实现了最高的分类精度,曲线下的面积 (AUC) 为0.93和平均精度 (AP) 为0.95.
- 语音分析,结合声学,时间和语言标记,被证明是有效的老年人虚弱的检测.
- SVM表现出强的表现,强调言语作为识别脆弱性的有价值标记.
结论:
- 基于语音的机器学习模型为老年人群的早期脆弱性查提供了一种新,可访问和可扩展的方法.
- 这种方法可以促进及时的干预,以提高强度和降低神经认知障碍的风险.
- 在临床环境中实施这些分类器可以增强老年人的预防护理策略.
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