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Articles Published Processes
3/29/2025 6:54:12 AM | Browse: 16 | Download: 60
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Received |
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2024-12-27 08:34 |
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Peer-Review Started |
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2024-12-27 08:34 |
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To Make the First Decision |
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Return for Revision |
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2025-01-15 11:50 |
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Revised |
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2025-01-25 07:32 |
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Second Decision |
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2025-02-18 02:37 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-02-18 06:38 |
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Articles in Press |
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2025-02-18 06:38 |
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Publication Fee Transferred |
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2025-01-29 03:07 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-02-28 00:44 |
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Publish the Manuscript Online |
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2025-03-29 06:54 |
| ISSN |
1948-9366 (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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2024. 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 |
Retrospective Study |
| Article Title |
Machine learning-based prediction of postoperative mortality risk after abdominal surgery
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Ji-Hong Yuan, Yong-Mei Jin, Jing-Ye Xiang, Shuang-Shuang Li, Ying-Xi Zhong, Shu-Liu Zhang and Bin Zhao |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Shanghai Municipal Health Commission Project |
No. 20214Y0284 |
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| Corresponding Author |
Bin Zhao, Chief Physician, MD, Department of General Surgery, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, No. 358 Datong Road, Gaoqiao Town, Pudong New District, Shanghai 201317, China. zhaobinpwk@163.com |
| Key Words |
Abdominal surgery; Postoperative death; Prediction; Machine learning; Risk assessment |
| Core Tip |
Individuals vary in terms of surgical risk, and assessments must be performed to evaluate this risk in order to provide all patients with the most appropriate perioperative care. Current risk assessments can be time consuming; we therefore aimed to use artificial intelligence to develop a model to predict the risk of 30-day mortality in patients undergoing abdominal surgery. Data from patients that underwent abdominal surgery in our hospital were used to construct six separate models with different machine learning algorithms. Four of the models demonstrated strong predictive performance, suggesting their potential clinical application. |
| Publish Date |
2025-03-29 06:54 |
| Citation |
<p>Yuan JH, Jin YM, Xiang JY, Li SS, Zhong YX, Zhang SL, Zhao B. Machine learning-based prediction of postoperative mortality risk after abdominal surgery. <i>World J Gastrointest Surg</i> 2025; 17(4): 103696</p> |
| URL |
https://www.wjgnet.com/1948-9366/full/v17/i4/103696.htm |
| DOI |
https://dx.doi.org/10.4240/wjgs.v17.i4.103696 |
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