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

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

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

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

Department of Computer Science & Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

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 language	English
Pages (from-to)	149-152
Number of pages	4
Journal	CEUR Workshop Proceedings
Volume	3124
State	Published - 2022
Event	Joint International Conference on Intelligent User Interfaces Workshops: APEx-UI, HAI-GEN, HEALTHI, HUMANIZE, TExSS, SOCIALIZE, IUI-WS 2022 - Virtual, Helsinki, Finland Duration: 21 Mar 2022 → 22 Mar 2022

Bibliographical note

Publisher Copyright:
© 2022 Copyright for this paper by its authors

Keywords

early dementia
information gain
questionnaire

Cite this

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title = "ReSmart-15: An Information Gain based Questionnaire for Early Dementia Detection",

abstract = "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.",

keywords = "early dementia, information gain, questionnaire",

author = "Hyeseong Park and Jung, {Myung Won Raymond} and Kim, {Ji Hye} and Uran Oh",

note = "Publisher Copyright: {\textcopyright} 2022 Copyright for this paper by its authors; Joint International Conference on Intelligent User Interfaces Workshops: APEx-UI, HAI-GEN, HEALTHI, HUMANIZE, TExSS, SOCIALIZE, IUI-WS 2022 ; Conference date: 21-03-2022 Through 22-03-2022",

year = "2022",

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TY - JOUR

T1 - ReSmart-15

T2 - Joint International Conference on Intelligent User Interfaces Workshops: APEx-UI, HAI-GEN, HEALTHI, HUMANIZE, TExSS, SOCIALIZE, IUI-WS 2022

AU - Park, Hyeseong

AU - Jung, Myung Won Raymond

AU - Kim, Ji Hye

AU - Oh, Uran

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - early dementia

KW - information gain

KW - questionnaire

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M3 - Conference article

AN - SCOPUS:85128821146

SN - 1613-0073

VL - 3124

SP - 149

EP - 152

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 21 March 2022 through 22 March 2022

ER -

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

Abstract

Bibliographical note

Keywords

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