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Articles Published Processes
3/15/2024 8:06:34 AM | Browse: 163 | Download: 399
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Received |
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2023-10-13 02:19 |
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Peer-Review Started |
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2023-10-13 02:20 |
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To Make the First Decision |
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Return for Revision |
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2023-11-02 08:15 |
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Revised |
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2023-11-08 01:26 |
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Second Decision |
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2023-12-04 02:18 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2023-12-11 06:25 |
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Articles in Press |
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2023-12-11 06:25 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2024-03-12 07:51 |
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Publish the Manuscript Online |
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2024-03-15 08:06 |
| ISSN |
2220-3230 (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) 2023. 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 |
Transplantation |
| Manuscript Type |
Systematic Reviews |
| Article Title |
Use of machine learning models for the prognostication of liver transplantation: A systematic review
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Gidion Chongo and Jonathan Soldera |
| ORCID |
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| Corresponding Author |
Jonathan Soldera, MD, MSc, Instructor, Department of Gastroenterology, University Of South Wales, Llantwit Rd, Pontypridd, Cardiff CF37 1DL, United Kingdom. jonathansoldera@gmail.com |
| Key Words |
Liver transplantation; Machine learning models; Prognostication; Allograft allocation; Artificial intelligence |
| Core Tip |
This systematic review highlights the promising role of machine learning (ML) models in improving prognostication for liver transplantation (LT). ML models consistently outperformed traditional scoring systems, demonstrating excellent predictive capabilities for various post-transplant complications, including mortality, sepsis, and acute kidney injury. The findings underscore the potential of ML in enhancing decision-making related to organ allocation and LT, representing a substantial advancement in prognostication methods. |
| Publish Date |
2024-03-15 08:06 |
| Citation |
Chongo G, Soldera J. Use of machine learning models for the prognostication of liver transplantation: A systematic review. World J Transplant 2023; In press |
| URL |
https://www.wjgnet.com/2220-3230/full/v14/i1/88891.htm |
| DOI |
https://dx.doi.org/10.5500/wjt.v14.i1.88891 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
