查尔马克:对痴呆症语言生物标志物的马尔科夫方法
Kevin Mekulu1, Faisal Aqlan2, Hui Yang1
1Complex Systems Monitoring, Modeling and Control Laboratory, Pennsylvania State University, University Park, PA 16802, USA.
medRxiv : the preprint server for health sciences
|August 12, 2025
概括
这项研究介绍了CharMark,一个可解释的AI框架,使用字符级别分析通过语音模式检测痴呆症. 它确定了用于早期认知衰退查的语言生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 痴呆症是一种广泛的神经退行性疾病,影响全球数百万人.
- 了解语言标记是识别语言认知衰退的关键.
- 目前的认知评估缺乏详细的语言洞察力.
研究的目的:
- 介绍CharMark,一个可解释的AI框架用于痴呆症的语言分析.
- 使用马尔科夫链模型,在字符层面描述语言的产生.
- 为了确定早期痴呆症检测的潜在语言生物标志物.
主要方法:
- 利用一级马尔科夫链模型来分析语音转录中的字符转换.
- 在痴呆症患者和健康对照人群中使用字符的计算稳定状态概率.
- 应用主要组件分析 (PCA) 和拉索逻辑回归用于特征分析和歧视.
主要成果:
- 在痴呆症患者和对照人群之间发现了字符使用的显著差异,包括空格 (暂停),"n"和"i".
- 基于认知状态的PCA揭示了不同的集群.
- 拉索逻辑回归证实了字符级特征的歧视力.
结论:
- 字符级语言特征可以作为认知衰退的可解释和可计算的生物标志物.
- 查尔马克为早期痴呆症查提供了一种轻量级,可扩展的方法.
- 这种方法适用于复杂或依赖音频的模型无法实现的设置.
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