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临床表现 临床表现

Natasha Clarke1,2, Christophe Bedetti1, Pierre-Briac Metayer1

  • 1Université de Montréal, Montréal, QC, Canada.

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概括

性差异影响阿尔茨海默病的分类. 连接语音分析表明,语言标记在女性中更敏感,这表明性别分层的方法对于准确的诊断和治疗至关重要.

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
  • 老年学是指老年学的学科.

背景情况:

  • 在阿尔茨海默病 (AD) 中观察到连接语音 (CS) 变化,并可能作为认知障碍的早期标志物.
  • 机器学习模型在使用CS功能对AD频谱中的个体进行分类方面表现有前途.
  • 细微的基于性别的语言差异可能会影响症状表现和分类准确性.

研究的目的:

  • 调查性对连接的语音特征和在阿尔茨海默氏症病谱中的分类表现的影响.
  • 确定性别分层机器学习模型是否可以提高认知衰退各个阶段的诊断准确度.

主要方法:

  • 从CCNA COMPASS-ND队列中的751名参与者的cookie盗窃场景描述分析.
  • 使用Python管道提取40个词汇,语义和语法CS特征.
  • 训练后勤回归模型用于AD,血管-AD,MCI,血管-MCI和SCI与认知不受损 (CU) 参与者的分类,分别对男性和女性进行.

主要成果:

  • 在主观认知障碍 (SCI),轻度认知障碍 (MCI) 和血管-AD (v-AD) 中,女性的分类精度 (AUC) 比男性更高,MCI 在校正后仍然显著.
  • 在SCI和MCI中,女性的分类表现高于机会,但男性的机会低于机会.
  • 对v-AD的分析揭示了两性之间不同的语音模式:v-AD的男性表现出流利但不那么详细的语言,有词汇访问困难,而女性表现出不流利,重复的语言,有更多的填充暂停.

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结论:

  • 按性别分层的分类模型显示出差异性表现,这表明在AD研究和临床实践中需要采用性别特异性的方法.
  • 来自连接语音的语言标记可能更敏感地检测AD频谱内的女性的认知衰退.
  • 这些发现强调了性别分层分析对准确诊断和阿尔茨海默病潜在治疗干预措施的重要性.