Computational Cognitive Assessment: Investigating the Use of an Intelligent Virtual Agent for the Detection of Early Signs of Dementia
Computational Cognitive Assessment: Investigating the Use of an Intelligent Virtual Agent for the Detection of Early Signs of Dementia
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DOI：
10.1109/icassp.2019.8682423
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
B. Mirheidari;D. Blackburn;R. O'Malley;Traci Walker;A. Venneri;M. Reuber;H. Christensen
中科院分区：
文献类型：
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作者：
B. Mirheidari;D. Blackburn;R. O'Malley;Traci Walker;A. Venneri;M. Reuber;H. Christensen
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，目前的诊断服务已经捉襟见肘，迫切需要将部分评估过程自动化。在以前的工作中，我们展示了一个分层工具是如何围绕智能虚拟代理（IVA）构建的，通过询问记忆探测问题来引发对话，能够准确区分患有神经退行性疾病（ND）和功能性记忆障碍（FMD）的人。在本文中，我们扩展了诊断类别的数量，包括健康的老年人对照（HC）以及轻度认知障碍（MCI）的人。我们还调查了IVA是否可以用于管理更标准的认知测试，如语言流畅性测试。在扩展特征集上训练的四向分类器实现了48%的准确率，通过仅使用22个最重要的特征（ROC-AUC：82%），准确率提高到62%。
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%).