在捷克语中使用计算分析进行语义语言流性评估
Jiri Pesek1, Hana Horakova1,2,3, Martin Vyhnalek3
1Department of Psychology, Faculty of Arts, Charles University, Prague, Czech Republic.
Applied neuropsychology. Adult
|September 24, 2025
概括
计算分析显示,在评估捷克患者轻度认知障碍 (MCI) 方面有前途. 这种方法,分析语义语言流 (SVF),提供与传统方法相比的见解,有助于MCI诊断.
科学领域:
- 神经心理学 神经心理学
- 计算语言学计算语言学
- 神经学 神经学
背景情况:
- 语义语言流 (SVF) 任务在神经心理评估中至关重要.
- SVF性能下降表明轻度认知障碍 (MCI) 和阿尔茨海默病 (AD).
- 传统的定性SVF分析具有局限性,并产生混合的结果.
研究的目的:
- 评估MCI评估计算方法在捷克共和国的诊断潜力.
- 将新型计算方法与SVF任务的传统定性分析进行比较.
- 分析动物和蔬菜类别的开关数 (NOS) 和平均集群大小 (MCS).
主要方法:
- 在语义语言流利任务 (动物和植物类别) 中分析NOS和MCS.
- 传统的定性分析与一种新的计算方法之间的比较.
- 使用曲线下的面积 (AUC) 对NOS和MCS的诊断值的评估.
主要成果:
- 在动物类别中,NOS在计算上显示出合理的诊断值,而MCS在两种方法中都显示出差值.
- 在蔬菜类别中,NOS在传统方法 (AUC=0.761) 和计算方法 (AUC=0.708) 中都显示出合理的诊断价值.
- 在大多数索引中,计算方法和传统方法之间没有发现诊断价值的显著差异.
结论:
- 对SVF任务的计算分析是捷克境内MCI评估的一个有希望的工具.
- 计算方法显示了与传统定性分析相比较的诊断潜力.
- 需要进一步改进,以解决诸如群体不平衡和多字短语处理等局限性.
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