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Articles Published Processes
7/27/2026 9:36:16 AM | Browse: 1 | Download: 0
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Received |
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2026-01-19 01:49 |
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Peer-Review Started |
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2026-01-19 01:51 |
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First Decision by Editorial Office Director |
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2026-01-29 08:06 |
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Return for Revision |
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2026-01-29 08:06 |
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Revised |
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2026-02-01 10:25 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-04-14 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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2026-04-14 08:34 |
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Articles in Press |
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2026-04-14 08:34 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-06-11 02:40 |
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Publish the Manuscript Online |
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2026-07-27 09:36 |
| 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 |
©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 |
Gastroenterology & Hepatology |
| Manuscript Type |
Opinion Review |
| Article Title |
Systematic assessment of mixed imputation methods and explainable machine learning
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Jia-Yi Li and Yan Zhao |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Yan Zhao, Chief Physician, Dean, Director, MD, PhD, Department of Gastric Surgery, Cancer Hospital of Dalian University of Technology, No. 44 Xiaoheyan Street, Dadong District, Shenyang 110042, Liaoning Province, China. drzhao@dlut.edu.cn |
| Key Words |
Gastric cancer; Machine learning; Survival prediction; Missing data imputation; Extra trees |
| Core Tip |
This opinion review evaluates the study by Lü et al, which introduces a hybrid imputation framework, HDI-MF-Gower, combined with an explainable extra trees classifier for predicting postoperative survival in gastric cancer. The study’s innovative approach addresses the challenges of missing clinical data by adapting the iterative imputation method MissForest. Although the model demonstrates clinical transparency and methodological rigor, its marginal improvement over existing ensemble methods and lack of external validation highlight areas for further research in multimodal data integration and multi-institutional validation. |
| Publish Date |
2026-07-27 09:36 |
| Citation |
Li JY, Zhao Y. Systematic assessment of mixed imputation methods and explainable machine learning. World J Gastrointest Surg 2026; 18(7): 119087
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| URL |
https://www.wjgnet.com/1948-9366/full/v18/i7/119087.htm |
| DOI |
https://doi.org/10.4240/wjgs.v18.i7.119087 |
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