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9/4/2026 7:27:23 AM | Browse: 4 | Download: 0
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Received |
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2026-05-13 01:40 |
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2026-05-13 23:59 |
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2026-06-02 07:15 |
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2026-06-02 07:15 |
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2026-06-15 16:38 |
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Second Decision by Editor |
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2026-06-30 02:43 |
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Final Decision by Editorial Office Director |
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2026-07-08 09:01 |
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Articles in Press |
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2026-07-08 09:01 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-08-26 00:30 |
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Publish the Manuscript Online |
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2026-09-04 05:57 |
| ISSN |
1948-9358 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| 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 |
Obstetrics & Gynecology |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
Early risk stratification of gestational diabetes using interpretable machine learning with first-trimester screening parameters
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Shuo-Mei Hung, Chie-Pein Chen, Fang-Ju Sun, Yi-Yung Chen, Liang-Kai Wang and Chen-Yu Chen |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Science and Technology Council of Taiwan |
No. NSTC 113-2314-B-195-016-MY3 |
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| Corresponding Author |
Chen-Yu Chen, Full Professor, MD, PhD, Department of Obstetrics and Gynecology, MacKay Memorial Hospital, No. 92, Section 2 Zhongshan North Road, Taipei 104217, Taiwan. f122481@mmh.org.tw |
| Key Words |
Gestational diabetes mellitus; Machine learning; First trimester; Risk stratification; Pregnancy-associated plasma protein A; Placental growth factor |
| Core Tip |
This retrospective cohort study developed an interpretable machine learning model for early risk stratification of gestational diabetes mellitus using first-trimester clinical and biomarker parameters. In 2756 pregnancies, gradient boosting with random oversampling achieved moderate discrimination with high negative predictive value. Model interpretation using SHapley Additive exPlanations and patient-level heatmaps identified maternal and placental factors as key contributors. This approach may support early identification of at-risk women and facilitate targeted preventive strategies before routine mid-pregnancy screening. |
| Publish Date |
2026-09-04 05:57 |
| Citation |
Hung SM, Chen CP, Sun FJ, Chen YY, Wang LK, Chen CY. Early risk stratification of gestational diabetes using interpretable machine learning with first-trimester screening parameters. World J Diabetes 2026; 17(9): 123276
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| URL |
https://www.wjgnet.com/1948-9358/full/v17/i9/123276.htm |
| DOI |
https://doi.org/10.4239/wjd.123276 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
