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4/26/2023 2:07:41 AM | Browse: 150 | Download: 709
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Received |
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2022-11-05 01:00 |
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Peer-Review Started |
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2022-11-01 13:30 |
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To Make the First Decision |
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Return for Revision |
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2023-01-30 02:27 |
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Revised |
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2023-02-12 17:49 |
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Second Decision |
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2023-03-17 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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2023-03-17 07:11 |
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Articles in Press |
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2023-03-17 07:11 |
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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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2023-03-26 16:27 |
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Publish the Manuscript Online |
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2023-04-25 23:32 |
| 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: 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 |
Respiratory System |
| Manuscript Type |
Observational Study |
| Article Title |
Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Qiu-Yu Li, Zi-Han Pan, Zhuo-Yu An, Zi-Zhen Wang, Yi-Ren Wang, Xi-Gong Zhang and Ning Shen |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
81900641 |
| the Research Funding of Peking University |
BMU2021MX020 and BMU2022MX008 |
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| Corresponding Author |
Ning Shen, Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, No. 49 Huayuan North Road, Haidian District, Beijing 100191, China. shenning1972@126.com |
| Key Words |
COVID-19; Clinical prediction model; Electron computed tomography; Machine learning |
| Core Tip |
The computed tomography (CT) score is a relatively objective and clinically accessible semiquantitative assessment tool for patients with coronavirus disease 2019 (COVID-19). The CT scores of common, severe, and critically ill patients showed different trends, and there were differences between the groups of patients as the disease progressed. Patients who are recovering from the disease can be monitored via CT at reduced intervals to reduce their radiation exposure and financial burden. The 2 wk CT scores of the patients were important for predicting disease deterioration in hospitalized patients who have an average admission severity rating. The qSOFA score, aspartate aminotransferase, oxygenation, and dyspnea were important for the prediction of severe/critical COVID-19 disease. |
| Publish Date |
2023-04-25 23:32 |
| Citation |
Li QY, An ZY, Pan ZH, Wang ZZ, Wang YR, Zhang XG, Shen N. Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores. World J Clin Cases 2023; 11(12): 2716-2728 |
| URL |
https://www.wjgnet.com/2307-8960/full/v11/i12/2716.htm |
| DOI |
https://dx.doi.org/10.12998/wjcc.v11.i12.2716 |
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