TY - GEN
T1 - A mental health database creation method with neuroscience-inspired search functions
AU - Raneva, Venera
AU - Kiyoki, Yasushi
N1 - Publisher Copyright:
© 2021 The authors and IOS Press.
PY - 2020/12/16
Y1 - 2020/12/16
N2 - 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.
AB - 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.
KW - 2-Phase Correlation Computing
KW - Combinatorial vector
KW - Mental health
KW - Neurotransmitters
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U2 - 10.3233/FAIA200838
DO - 10.3233/FAIA200838
M3 - Conference contribution
AN - SCOPUS:85099198744
T3 - Frontiers in Artificial Intelligence and Applications
SP - 329
EP - 342
BT - Information Modelling and Knowledge Bases XXXII
A2 - Tropmann-Frick, Marina
A2 - Thalheim, Bernhard
A2 - Jaakkola, Hannu
A2 - Kiyoki, Yasushi
A2 - Yoshida, Naofumi
PB - IOS Press BV
T2 - 30th International conference on Information Modeling and Knowledge Bases, EJC 2020
Y2 - 8 June 2020 through 9 June 2020
ER -