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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Computational Cognitive Assessment: Investigating the Use of an Intelligent Virtual Agent for the Detection of Early Signs of Dementia
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Computational Cognitive Assessment: Investigating the Use of an Intelligent Virtual Agent for the Detection of Early Signs of Dementia

机译：计算性认知评估：研究使用智能虚拟代理程序检测痴呆的早期征兆

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The ageing population has caused a marked increased in the number of people with cognitive decline linked with dementia. Thus, current diagnostic services are overstretched, and there is an urgent need for automating parts of the assessment process. In previous work, we demonstrated how a stratification tool built around an Intelligent Virtual Agent (IVA) eliciting a conversation by asking memory-probing questions, was able to accurately distinguish between people with a neuro-degenerative disorder (ND) and a functional memory disorder (FMD). In this paper, we extend the number of diagnostic classes to include healthy elderly controls (HCs) as well as people with mild cognitive impairment (MCI). We also investigate whether the IVA may be used for administering more standard cognitive tests, like the verbal fluency tests. A four-way classifier trained on an extended feature set achieved 48% accuracy, which improved to 62% by using just the 22 most significant features (ROC-AUC: 82%).
机译：人口老龄化导致与痴呆症相关的认知能力下降的人数显着增加。因此，当前的诊断服务过度紧张，并且迫切需要使评估过程的各个部分自动化。在先前的工作中，我们演示了围绕智能虚拟代理（IVA）构建的分层工具如何通过询问内存探测问题来引发对话，如何准确地区分患有神经退行性疾病（ND）和功能性记忆障碍的人（FMD）。在本文中，我们扩展了诊断类别的数量，以包括健康的老年人对照（HCs）以及轻度认知障碍（MCI）的人群。我们还调查了IVA是否可用于管理更多标准的认知测验，例如口语流利度测验。在扩展功能集上进行训练的四向分类器实现了48％的准确度，仅使用22个最重要的功能即可将其提高到62％（ROC-AUC：82％）。

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