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Predicting Dementia Screening and Staging Scores from Semantic Verbal Fluency Performance

机译:从语义语言流利度表现预测痴呆症筛查和分期分数

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The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as the Semantic Verbal Fluency (SVF), demand little time. With this as a starting point, we investigate the relation between SVF results and MMSE/CDR-SOB scores. We use regression models to predict scores based on persons' SVF performance. Over a set of 179 patients with different degree of dementia, we achieve a mean absolute error of of 2.2 for MMSE (range 0-30) and 1.7 for CDR-SOB (range 0-18). True and predicted scores agree with a Cohen's κ of 0.76 for MMSE and 0.52 for CDR-SOB. We conclude that our approach has potential to serve as a cheap dementia screening, possibly even in non-clinical settings.
机译:标准痴呆筛查工具迷你精神状态检查(MMSE)和标准痴呆分期工具临床痴呆症评定量表(CDR)是回答问题的突出方法,用于分别是否有痴呆症和痴呆症严重程度。这些方法是耗时的,需要受过良好教育的人员进行管理。相反,认知测试,例如语义口语流畅(SVF),需求量小的时间。在此作为起点,我们调查SVF结果与MMSE / CDR-SOB分数之间的关系。我们使用回归模型来基于人员的SVF性能来预测分数。在一组179例患有不同程度的痴呆症的患者中,我们达到了2.2的平均绝对误差,对于CDR-SOB(范围0-18),MMSE(范围0-30)和1.7。真正的和预测的分数与CDR-SOB的MMSE和0.52的COHEN的κ为0.76。我们得出结论,我们的方法有可能作为廉价痴呆筛查,甚至可能在非临床环境中。

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