基于语言的轻度认知障碍检测使用信息损失
Ali Pourramezan Fard1, Mohammad H Mahoor2, Muath Alsuhaibani3
1Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO 80208, USA.
Computers in biology and medicine
|May 19, 2024
概括
这项研究使用自然语言处理 (NLP) 来从视频采访中检测老年人的轻度认知障碍 (MCI). 人工智能模型实现了84.75%的准确性,为早期认知评估提供了一个新的工具.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 神经科学是一个神经科学.
背景情况:
- 轻度认知障碍 (MCI) 影响老年人,需要早期检测方法.
- 目前的诊断方法可能是侵入性的或耗时的.
- 基于视频的评估为非侵入性认知查提供了一个潜在的途径.
研究的目的:
- 开发和评估使用自然语言处理 (NLP) 的深度学习框架,以区分轻度认知障碍 (MCI) 和正常认知 (NC).
- 在I-CONECT研究中分析视频采访的成绩单,用于认知状态分类.
- 引入一个新的损失函数 (InfoLoss),以提高分类准确性.
主要方法:
- 利用基于变压器的模块 (句子嵌入和句子交叉注意) 来分析采访成绩单.
- 从句子序列中提取时间特征以进行分类.
- 使用多层感知器 (MLP) 将受试者分类为MCI或NC.
- 引入了一个新的InfoLoss功能,以提高模型的稳定性和准确性.
主要成果:
- 拟议的NLP框架在区分MCI和NC时实现了平均曲线下面积 (AUC) 的84.75%
- 句子嵌入和句子交叉注意模块有效地捕捉了语言和时间特征.
- 信息丢失功能有助于提高分类性能.
结论:
- 深度学习NLP框架显示出从视频采访数据中准确识别老年人的MCI的巨大潜力.
- 这种方法为早期认知障碍检测提供了一个有希望的,非侵入性的方法.
- 在更大的数据集上进行进一步验证是有必要的,以确认临床效用.
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