从自发言语进行认知评估的跨语言语言标记
Bao Hoang1, Yijiang Pang1, Hiroko Dodge2
1Department of Computer Science and Engineering, Michigan State University, USA.
概括
这项研究表明,使用来自英语和中文演讲的语言标记可以帮助检测轻度认知障碍 (MCI) 并预测认知分数. 跨语言标志物可以从自发言语中改善早期MCI的识别.
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:
- 轻度认知障碍 (MCI) 是痴呆症的前体,需要早期检测才能进行干预.
- 目前的检测方法可能是侵入性的或昂贵的.
- 自发言语中的语言标记提供了一个非侵入性的,可访问的方法来识别MCI.
研究的目的:
- 调查来自自发言语的双语语言标志物的有效性,用于MCI检测.
- 用这些语言标记器来预测迷你精神状态考试 (MMSE) 的分数.
- 开发一个强大的MCI识别和认知评估框架.
主要方法:
- 从自发言语中提取双语声学,语义和语法特征.
- 采用了一个框架来解决小样本规模和不平衡的人口统计学模型的复杂性.
- 利用多任务学习方法,将临床分类预测作为MMSE得分预测的先决条件.
主要成果:
- 实现了MCI检测的平均平衡精度为78.2%.
- 在MMSE得分预测中获得了2.705的平均根平均平方误差 (RMSE).
- 证明跨语言语言标志物可以从自发言语中增强MCI检测.
结论:
- 从自发言语中获得的双语语言标志物对于识别MCI是有效的.
- 这种方法对早期和可访问的认知衰退检测有希望.
- 开发的框架为临床研究和潜在的诊断支持提供了有价值的工具.
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