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8/19/2026 3:52:23 AM | Browse: 7 | Download: 1
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Received |
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2025-12-30 06:04 |
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Peer-Review Started |
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2025-12-30 06:05 |
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First Decision by Editorial Office Director |
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2026-01-29 09:21 |
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2026-01-29 09:21 |
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Revised |
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2026-02-02 02:17 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-02-11 02:38 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-02-12 08:56 |
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Articles in Press |
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2026-02-12 08:56 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-08-10 00:19 |
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Publish the Manuscript Online |
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2026-08-19 01:55 |
| ISSN |
1007-9327 (print) and 2219-2840 (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) 2026. 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 |
Gastroenterology & Hepatology |
| Manuscript Type |
Editorial |
| Article Title |
Bid farewell to single indicators: Machine learning models integrating multidimensional data lead thrombosis risk prediction into a new
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Ying Su, Dong-Xia Wang, Yi-Qun Zhao and Xue Xing |
| Funding Agency and Grant Number |
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| Corresponding Author |
Xue Xing, Doctorate Student, Department of Clinical Laboratory, The Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Dalian 116021, Liaoning Province, China. dyeyxx39198645@126.com |
| Key Words |
Nonvariceal gastrointestinal bleeding; Thromboembolism; Machine learning; Risk prediction; Precision medicine; Classification boosting algorithm; D-dimer |
| Core Tip |
This multicenter study demonstrates that machine learning models based on routine clinical data can effectively predict thrombotic risk in patients with nonvariceal gastrointestinal bleeding (NVGIB), outperforming the traditional D-dimer biomarker. The Classification Boosting Algorithm model exhibited the best performance, aiding in the clinical identification of high-risk patients for early intervention while avoiding excessive monitoring in low-risk individuals, thereby advancing thromboprophylaxis strategies toward precision medicine. |
| Publish Date |
2026-08-19 01:55 |
| Citation |
Su Y, Wang DX, Zhao YQ, Xing X. Bid farewell to single indicators: Machine learning models integrating multidimensional data lead thrombosis risk prediction into a new. World J Gastroenterol 2026; 32(34): 118337
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| URL |
https://www.wjgnet.com/1007-9327/full/v32/i34/118337.htm |
| DOI |
https://doi.org/10.3748/wjg.118337 |
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