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A measurement method for mental health based on dynamic multimodal feature recognition

INTRODUCTION: The number of college students with mental problems has increased significantly, particularly during COVID-19. However, the clinical features of early-stage psychological problems are subclinical, so the optimal intervention treatment period can easily be missed. Artificial intelligenc...

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Detalles Bibliográficos
Autores principales: Xu, Haibo, Wu, Xiang, Liu, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816124/
https://www.ncbi.nlm.nih.gov/pubmed/36620271
http://dx.doi.org/10.3389/fpubh.2022.990235
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author Xu, Haibo
Wu, Xiang
Liu, Xin
author_facet Xu, Haibo
Wu, Xiang
Liu, Xin
author_sort Xu, Haibo
collection PubMed
description INTRODUCTION: The number of college students with mental problems has increased significantly, particularly during COVID-19. However, the clinical features of early-stage psychological problems are subclinical, so the optimal intervention treatment period can easily be missed. Artificial intelligence technology can efficiently assist in assessing mental health problems by mining the deep correlation of multi-dimensional data of patients, providing ideas for solving the screening of normal psychological problems in large-scale college students. Therefore, we propose a mental health assessment method that integrates traditional scales and multimodal intelligent recognition technology to support the large-scale and normalized screening of mental health problems in colleges and universities. METHODS: Firstly, utilize the psychological assessment scales based on human-computer interaction to conduct health questionnaires based on traditional methods. Secondly, integrate machine learning technology to identify the state of college students and assess the severity of psychological problems. Finally, the experiments showed that the proposed multimodal intelligent recognition method has high accuracy and can better proofread normal scale results. This study recruited 1,500 students for this mental health assessment. RESULTS: The results showed that the incidence of moderate or higher stress, anxiety, and depression was 36.3, 48.1, and 23.0%, which is consistent with the results of our multiple targeted tests. CONCLUSION: Therefore, the interactive multimodality emotion recognition method proposed provides an effective way for large-scale mental health screening, monitoring, and intervening in college students' mental health problems.
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spelling pubmed-98161242023-01-07 A measurement method for mental health based on dynamic multimodal feature recognition Xu, Haibo Wu, Xiang Liu, Xin Front Public Health Public Health INTRODUCTION: The number of college students with mental problems has increased significantly, particularly during COVID-19. However, the clinical features of early-stage psychological problems are subclinical, so the optimal intervention treatment period can easily be missed. Artificial intelligence technology can efficiently assist in assessing mental health problems by mining the deep correlation of multi-dimensional data of patients, providing ideas for solving the screening of normal psychological problems in large-scale college students. Therefore, we propose a mental health assessment method that integrates traditional scales and multimodal intelligent recognition technology to support the large-scale and normalized screening of mental health problems in colleges and universities. METHODS: Firstly, utilize the psychological assessment scales based on human-computer interaction to conduct health questionnaires based on traditional methods. Secondly, integrate machine learning technology to identify the state of college students and assess the severity of psychological problems. Finally, the experiments showed that the proposed multimodal intelligent recognition method has high accuracy and can better proofread normal scale results. This study recruited 1,500 students for this mental health assessment. RESULTS: The results showed that the incidence of moderate or higher stress, anxiety, and depression was 36.3, 48.1, and 23.0%, which is consistent with the results of our multiple targeted tests. CONCLUSION: Therefore, the interactive multimodality emotion recognition method proposed provides an effective way for large-scale mental health screening, monitoring, and intervening in college students' mental health problems. Frontiers Media S.A. 2022-12-23 /pmc/articles/PMC9816124/ /pubmed/36620271 http://dx.doi.org/10.3389/fpubh.2022.990235 Text en Copyright © 2022 Xu, Wu and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Xu, Haibo
Wu, Xiang
Liu, Xin
A measurement method for mental health based on dynamic multimodal feature recognition
title A measurement method for mental health based on dynamic multimodal feature recognition
title_full A measurement method for mental health based on dynamic multimodal feature recognition
title_fullStr A measurement method for mental health based on dynamic multimodal feature recognition
title_full_unstemmed A measurement method for mental health based on dynamic multimodal feature recognition
title_short A measurement method for mental health based on dynamic multimodal feature recognition
title_sort measurement method for mental health based on dynamic multimodal feature recognition
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816124/
https://www.ncbi.nlm.nih.gov/pubmed/36620271
http://dx.doi.org/10.3389/fpubh.2022.990235
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