A mental health database creation method with neuroscience-inspired search functions

Venera Raneva, Yasushi Kiyoki

研究成果: Conference contribution

抄録

Mental health, an essential factor for maintaining a high quality of life, is determined by one’s nutritional, physical, and psychological situations. Since mental health is influenced by multiple factors, a multidisciplinary approach is effective. Due to the complexity of this mechanism, most non-specialists have little knowledge and access to the related information. There are multiple factors that influence one’s mental health, such as nutrition, physical activities, daily habits, and personal cognitive characteristics. Because of this complexity, it can be hard for non-specialists to find and implement appropriate methods for improving their mental health. This paper presents the 2-Phase Correlation Computing method for interpreting the characteristics of each emotion/mental state, nutrients, exercises, life habits with a vector space. The vector space reflects the roles of neurotransmitters. The 2-Phase Correlation Computing extracts the information expected to be most relevant to the user’s request. In this method, expert knowledge, characteristics of emotions, and mental states are defined in the “Requests” Matrix, and each stimulus into “Nutrients”, “Exercises”, and “Life Habits” Matrixes. “Nutrients”, “Exercises”, and “Life Habits” are expressed and computed to as “Stimuli”. In short, this method introduces logos to the chaotic world of decision making in mental health.

本文言語English
ホスト出版物のタイトルInformation Modelling and Knowledge Bases XXXII
編集者Marina Tropmann-Frick, Bernhard Thalheim, Hannu Jaakkola, Yasushi Kiyoki, Naofumi Yoshida
出版社IOS Press BV
ページ329-342
ページ数14
ISBN(電子版)9781643681405
DOI
出版ステータスPublished - 2020 12 16
イベント30th International conference on Information Modeling and Knowledge Bases, EJC 2020 - Virtual, Online, Germany
継続期間: 2020 6 82020 6 9

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
333
ISSN(印刷版)0922-6389

Conference

Conference30th International conference on Information Modeling and Knowledge Bases, EJC 2020
CountryGermany
CityVirtual, Online
Period20/6/820/6/9

ASJC Scopus subject areas

  • Artificial Intelligence

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