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Articles Published Processes
8/8/2025 10:23:14 AM | Browse: 17 | Download: 82
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Received |
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2025-03-04 07:58 |
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Peer-Review Started |
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2025-03-04 07:58 |
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To Make the First Decision |
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Return for Revision |
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2025-04-03 08:43 |
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Revised |
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2025-04-17 08:30 |
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Second Decision |
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2025-06-25 02:43 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-06-25 08:49 |
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Articles in Press |
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2025-06-25 08:49 |
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Publication Fee Transferred |
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2025-04-18 01:50 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-07-22 00:15 |
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Publish the Manuscript Online |
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2025-08-08 10:23 |
| ISSN |
2220-3206 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Psychiatry |
| Manuscript Type |
Observational Study |
| Article Title |
Machine learning-based nomogram for predicting depressive symptoms in women: A cross-sectional study in Guangdong Province, China
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Jia-Min Chen, Mei Rao, Yu-Ting Wei, Qiong-Gui Zhou, Jun-Long Tao, Shi-Bin Wang and Bo Bi |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Longyan City Science and Technology Plan Project |
2024 LYF17067 |
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| Corresponding Author |
Bo Bi, School of Public Health, Hainan Medical University, Xueyuan Road, Longhua District, Haikou 571199, Hainan Province, China. bibo@hainmc.edu.cn |
| Key Words |
Depressive symptoms; Women’s mental health; Machine learning; Predictive modeling; Shapley additive explanations; Nomogram; Guangdong province |
| Core Tip |
This study leverages machine learning to develop a highly accurate predictive model for depressive symptoms in women. Ablation studies systematically validated the critical contributions of these top-ranked Shapley Additive exPlanations features, demonstrating significant performance degradation upon their removal. The light gradient boosting machine model achieved superior performance, supported by Shapley Additive exPlanations for interpretability and a nomogram for clinical application. This innovative approach offers a practical tool for early detection and personalized intervention, addressing the limitations of traditional methods. Findings highlight the potential of machine learning to enhance women’s mental health outcomes, with implications for improving diagnostic precision and treatment strategies in diverse clinical settings. |
| Publish Date |
2025-08-08 10:23 |
| Citation |
<p>Chen JM, Rao M, Wei YT, Zhou QG, Tao JL, Wang SB, Bi B. Machine learning-based nomogram for predicting depressive symptoms in women: A cross-sectional study in Guangdong Province, China. <i>World J Psychiatry</i> 2025; 15(8): 106622</p> |
| URL |
https://www.wjgnet.com/2220-3206/full/v15/i8/106622.htm |
| DOI |
https://dx.doi.org/10.5498/wjp.v15.i8.106622 |
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