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A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly

Yingjie Wang, Xuzhe Wang, Li Zhao, Kyle Jones

Journal of Affective Disorders, Volume: 369, Pages: 329 - 337

Swansea University Author: Kyle Jones

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DOI (Published version): 10.1016/j.jad.2024.09.147

Abstract

Background: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China as a sample to predict the risk factors of depress...

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Published in: Journal of Affective Disorders
ISSN: 0165-0327 1573-2517
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa67859
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spelling v2 67859 2024-09-30 A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly f1e04033c100c34224c7c0d35e059658 Kyle Jones Kyle Jones true false 2024-09-30 PSYS Background: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China as a sample to predict the risk factors of depression in the elderly population from both holistic and individual level. Methods: A total of participants met the inclusion criteria from the fourth waves of the China Health and Retirement Longitudinal Study (CHARLS) were analyzed with ML algorithms. The level of depression was assessed by the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Results: The current study found top 5 factors that were important for predicting depression in the elderly population in China, including average sleep time, gender, age, social activities and nap time during the day. The results also provide reliable diagnostic likelihood at the individual level to support clinicians identify the most impactful factors contributing to patient depression. Our findings also suggested that activities such as interacting with friends and play ma-Jong, chess or join community clubs may have a positive collaborative effect for elderly's mental health. Conclusions: Holistic approaches are an effective method of deriving and interpreting sophisticated models of mental health in elderly populations. More detailed information about a patient's demographics, medical history, sleeping patterns and social/leisure activities can help to inform policy and treatment interventions on a population and individual level. Large scale surveys such as CHARLS are effective methods for testing the most accurate models, however, further research using professional clinical input could further advance the field. Journal Article Journal of Affective Disorders 369 329 337 Elsevier BV 0165-0327 1573-2517 Machine learning, CHARLS, elderly population, aging, risk factors 15 1 2025 2025-01-15 10.1016/j.jad.2024.09.147 COLLEGE NANME Psychology School COLLEGE CODE PSYS Swansea University SU Library paid the OA fee (TA Institutional Deal) National Social Science Fund of China 22CRK01 2024-10-10T11:53:06.7703930 2024-09-30T13:05:55.8086765 Faculty of Medicine, Health and Life Sciences School of Psychology Yingjie Wang 1 Xuzhe Wang 2 Li Zhao 3 Kyle Jones 4 67859__32578__16511279abc247f7a416266472284499.pdf 67859.VoR.pdf 2024-10-10T11:49:16.5793613 Output 2257697 application/pdf Version of Record true © 2024 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly
spellingShingle A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly
Kyle Jones
title_short A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly
title_full A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly
title_fullStr A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly
title_full_unstemmed A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly
title_sort A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly
author_id_str_mv f1e04033c100c34224c7c0d35e059658
author_id_fullname_str_mv f1e04033c100c34224c7c0d35e059658_***_Kyle Jones
author Kyle Jones
author2 Yingjie Wang
Xuzhe Wang
Li Zhao
Kyle Jones
format Journal article
container_title Journal of Affective Disorders
container_volume 369
container_start_page 329
publishDate 2025
institution Swansea University
issn 0165-0327
1573-2517
doi_str_mv 10.1016/j.jad.2024.09.147
publisher Elsevier BV
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str School of Psychology{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}School of Psychology
document_store_str 1
active_str 0
description Background: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China as a sample to predict the risk factors of depression in the elderly population from both holistic and individual level. Methods: A total of participants met the inclusion criteria from the fourth waves of the China Health and Retirement Longitudinal Study (CHARLS) were analyzed with ML algorithms. The level of depression was assessed by the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Results: The current study found top 5 factors that were important for predicting depression in the elderly population in China, including average sleep time, gender, age, social activities and nap time during the day. The results also provide reliable diagnostic likelihood at the individual level to support clinicians identify the most impactful factors contributing to patient depression. Our findings also suggested that activities such as interacting with friends and play ma-Jong, chess or join community clubs may have a positive collaborative effect for elderly's mental health. Conclusions: Holistic approaches are an effective method of deriving and interpreting sophisticated models of mental health in elderly populations. More detailed information about a patient's demographics, medical history, sleeping patterns and social/leisure activities can help to inform policy and treatment interventions on a population and individual level. Large scale surveys such as CHARLS are effective methods for testing the most accurate models, however, further research using professional clinical input could further advance the field.
published_date 2025-01-15T11:53:05Z
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score 11.035349

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