ReSmart-15: An Information Gain based Questionnaire for Early Dementia Detection

Hyeseong Park, Myung Won Raymond Jung, Ji Hye Kim, Uran Oh

Research output: Contribution to journalConference articlepeer-review


To build an effective questionnaire for detecting early dementia, we propose ReSmart-15 which is a dementia detection questionnaire that includes daily behavior-based questions in five categories (i.e., attention (3Q), spatial ability (3Q), spatiotemporal ability (3Q), memory (3Q), and thinking ability (3Q)). As for the evaluation, we first collected responses from two different screening tests with 87 participants. Then we used a machine learning method called "information gain" ranking to show the effectiveness of ReSmart-15 compared to another representative screening test. As a result, we found that the top 2 questions were from ReSmart-15, and 60 percent of ReSmart-15 questions were in the top 10.

Original languageEnglish
Pages (from-to)149-152
Number of pages4
JournalCEUR Workshop Proceedings
StatePublished - 2022
EventJoint International Conference on Intelligent User Interfaces Workshops: APEx-UI, HAI-GEN, HEALTHI, HUMANIZE, TExSS, SOCIALIZE, IUI-WS 2022 - Virtual, Helsinki, Finland
Duration: 21 Mar 202222 Mar 2022

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  • early dementia
  • information gain
  • questionnaire


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