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Depression prediction based on LassoNet-RNN model: A longitudinal study
Depression has become a widespread health concern today. Understanding the influencing factors can promote human mental health as well as provide a basis for exploring preventive measures. Combining LassoNet with recurrent neural network (RNN), this study constructed a screening model ,LassoNet-RNN,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570602/ https://www.ncbi.nlm.nih.gov/pubmed/37842633 http://dx.doi.org/10.1016/j.heliyon.2023.e20684 |
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author | Han, Jiatong Li, Hao Lin, Han Wu, Pingping Wang, Shidan Tu, Juan Lu, Jing |
author_facet | Han, Jiatong Li, Hao Lin, Han Wu, Pingping Wang, Shidan Tu, Juan Lu, Jing |
author_sort | Han, Jiatong |
collection | PubMed |
description | Depression has become a widespread health concern today. Understanding the influencing factors can promote human mental health as well as provide a basis for exploring preventive measures. Combining LassoNet with recurrent neural network (RNN), this study constructed a screening model ,LassoNet-RNN, for identifying influencing factors of individual depression. Based on multi-wave surveys of China Health and Retirement Longitudinal Study (CHARLS) dataset (11,661 observations), we analyzed the multivariate time series data and recognized 27 characteristic variables selected from four perspectives: demographics, health-related risk factors, household economic status, and living environment. Additionally, the importance rankings of the characteristic variables were obtained. These results offered insightful recommendations for theoretical developments and practical decision making in public health. |
format | Online Article Text |
id | pubmed-10570602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-105706022023-10-14 Depression prediction based on LassoNet-RNN model: A longitudinal study Han, Jiatong Li, Hao Lin, Han Wu, Pingping Wang, Shidan Tu, Juan Lu, Jing Heliyon Research Article Depression has become a widespread health concern today. Understanding the influencing factors can promote human mental health as well as provide a basis for exploring preventive measures. Combining LassoNet with recurrent neural network (RNN), this study constructed a screening model ,LassoNet-RNN, for identifying influencing factors of individual depression. Based on multi-wave surveys of China Health and Retirement Longitudinal Study (CHARLS) dataset (11,661 observations), we analyzed the multivariate time series data and recognized 27 characteristic variables selected from four perspectives: demographics, health-related risk factors, household economic status, and living environment. Additionally, the importance rankings of the characteristic variables were obtained. These results offered insightful recommendations for theoretical developments and practical decision making in public health. Elsevier 2023-10-05 /pmc/articles/PMC10570602/ /pubmed/37842633 http://dx.doi.org/10.1016/j.heliyon.2023.e20684 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Han, Jiatong Li, Hao Lin, Han Wu, Pingping Wang, Shidan Tu, Juan Lu, Jing Depression prediction based on LassoNet-RNN model: A longitudinal study |
title | Depression prediction based on LassoNet-RNN model: A longitudinal study |
title_full | Depression prediction based on LassoNet-RNN model: A longitudinal study |
title_fullStr | Depression prediction based on LassoNet-RNN model: A longitudinal study |
title_full_unstemmed | Depression prediction based on LassoNet-RNN model: A longitudinal study |
title_short | Depression prediction based on LassoNet-RNN model: A longitudinal study |
title_sort | depression prediction based on lassonet-rnn model: a longitudinal study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570602/ https://www.ncbi.nlm.nih.gov/pubmed/37842633 http://dx.doi.org/10.1016/j.heliyon.2023.e20684 |
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