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Description

Abstract: For detecting early signs of dementia, monitoring technology has been actively investigated due to the low diagnostic
coverage as well as the requirement for early intervention. Although language features have been used for detecting the
language dysfunctions resulting from dementia in neuropsychological tests, features that can be extracted by regular
conversations remain unexplored. Here, we propose a feature to characterize the atypical repetition of words on
different days which is observed in patients with dementia. We tested it on data obtained from a daily monitoring
service for eight elderly people, including two who had been diagnosed with dementia. We found that our feature
outperformed the existing linguistic features used in previous studies, such as vocabulary richness and repetitiveness,
in terms of effect size and AUC score. The results suggest that the use of our proposed feature holds promise for
improving detection performance in everyday situations such as regular monitoring.

Learning Objective 1:
After attending in this session, the learner should be better able to:
-Apply the latest findings from research of data science and informatics approaches to support medical dicision makings related to diagnosis and intervention for dementia
-Create and sustain multidisciplinary collaborations in the biomedical research community to expand access to diverse expertise, sophisticated technologies, and unique tools and resources

Authors:

Kaoru Shinkawa (Presenter)
IBM Reseach - Tokyo

Yasunori Yamada, IBM Reseach - Tokyo

Presentation Materials:

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