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Articles Published Processes
12/23/2021 2:14:13 PM | Browse: 306 | Download: 781
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Received |
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2021-08-10 09:56 |
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Peer-Review Started |
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2021-08-10 09:56 |
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To Make the First Decision |
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Return for Revision |
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2021-09-02 00:55 |
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Revised |
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2021-09-15 07:43 |
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Second Decision |
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2021-11-02 03:56 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Company Editor-in-Chief |
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2021-11-03 01:16 |
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Articles in Press |
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2021-11-03 01:16 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2021-10-27 10:32 |
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Typeset the Manuscript |
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2021-12-09 03:21 |
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Publish the Manuscript Online |
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2021-12-23 14:14 |
| ISSN |
2307-8960 (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) 2021. 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 |
Urology & Nephrology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Machine learning approach to predict acute kidney injury after liver surgery
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Jun-Feng Dong, Qiang Xue, Ting Chen, Yuan-Yu Zhao, Hong Fu, Wen-Yuan Guo and Jun-Song Ji |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Jun-Song Ji, MM, PhD, Associate Professor, Department of Organ Transplantation, Changzheng Hospital, Navy Medical University, No. 415, Fengyang Road, Huangpu District, Shanghai 200003, China. 974938677@qq.com |
| Key Words |
Machine learning; Liver cancer; Surgery; Acute kidney injury, Prediction |
| Core Tip |
Acute kidney injury (AKI) is a relatively common complication after liver surgery and has a negative impact on the long-term patient prognosis. Early detection and timely intervention are key for minimizing the negative impacts of AKI. Machine learning has become increasingly better integrated with clinical medicine. In our retrospective study, we established a real-time prediction model based on machine learning algorithms. The final models show high power to discriminate AKI events. |
| Publish Date |
2021-12-23 14:14 |
| Citation |
Dong JF, Xue Q, Chen T, Zhao YY, Fu H, Guo WY, Ji JS. Machine learning approach to predict acute kidney injury after liver surgery. World J Clin Cases 2021; 9(36): 11255-11264 |
| URL |
https://www.wjgnet.com/2307-8960/full/v9/i36/11255.htm |
| DOI |
https://dx.doi.org/10.12998/wjcc.v9.i36.11255 |
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