Publication Name	World Journal of Clinical Cases
Manuscript ID	81109
Country	China

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
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
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.
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

Received	2022-11-05 01:00
Peer-Review Started	2022-11-01 13:30
To Make the First Decision
Return for Revision	2023-01-30 02:27
Revised	2023-02-12 17:49
Second Decision	2023-03-17 02:37
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2023-03-17 07:11
Articles in Press	2023-03-17 07:11
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2023-03-26 16:27

ISSN	2307-8960 (online)
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