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Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability

In this article, we present a deep learning model of human psychology that can predict one’s current age and future well-being. We used the model to demonstrate that one’s baseline well-being is not the determining factor of future well-being, as posited by hedonic treadmill theory. Further, we have...

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Autores principales: Galkin, Fedor, Kochetov, Kirill, Keller, Michelle, Zhavoronkov, Alex, Etcoff, Nancy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271294/
https://www.ncbi.nlm.nih.gov/pubmed/35723468
http://dx.doi.org/10.18632/aging.204061
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author Galkin, Fedor
Kochetov, Kirill
Keller, Michelle
Zhavoronkov, Alex
Etcoff, Nancy
author_facet Galkin, Fedor
Kochetov, Kirill
Keller, Michelle
Zhavoronkov, Alex
Etcoff, Nancy
author_sort Galkin, Fedor
collection PubMed
description In this article, we present a deep learning model of human psychology that can predict one’s current age and future well-being. We used the model to demonstrate that one’s baseline well-being is not the determining factor of future well-being, as posited by hedonic treadmill theory. Further, we have created a 2D map of human psychotypes and identified the regions that are most vulnerable to depression. This map may be used to provide personalized recommendations for maximizing one’s future well-being.
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spelling pubmed-92712942022-07-13 Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability Galkin, Fedor Kochetov, Kirill Keller, Michelle Zhavoronkov, Alex Etcoff, Nancy Aging (Albany NY) Research Paper In this article, we present a deep learning model of human psychology that can predict one’s current age and future well-being. We used the model to demonstrate that one’s baseline well-being is not the determining factor of future well-being, as posited by hedonic treadmill theory. Further, we have created a 2D map of human psychotypes and identified the regions that are most vulnerable to depression. This map may be used to provide personalized recommendations for maximizing one’s future well-being. Impact Journals 2022-06-20 /pmc/articles/PMC9271294/ /pubmed/35723468 http://dx.doi.org/10.18632/aging.204061 Text en Copyright: © 2022 Galkin et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Galkin, Fedor
Kochetov, Kirill
Keller, Michelle
Zhavoronkov, Alex
Etcoff, Nancy
Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability
title Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability
title_full Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability
title_fullStr Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability
title_full_unstemmed Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability
title_short Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability
title_sort optimizing future well-being with artificial intelligence: self-organizing maps (soms) for the identification of islands of emotional stability
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271294/
https://www.ncbi.nlm.nih.gov/pubmed/35723468
http://dx.doi.org/10.18632/aging.204061
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