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 [PMID: 37214568 DOI: 10.12998/wjcc.v11.i12.2716]
Corresponding Author of This Article
Ning Shen, MD, Chief Doctor, Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, No. 49 Huayuan North Road, Haidian District, Beijing 100191, China. shenning1972@126.com
Research Domain of This Article
Respiratory System
Article-Type of This Article
Observational Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
World J Clin Cases. Apr 26, 2023; 11(12): 2716-2728 Published online Apr 26, 2023. doi: 10.12998/wjcc.v11.i12.2716
Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores
Qiu-Yu Li, Zhuo-Yu An, Zi-Han Pan, Zi-Zhen Wang, Yi-Ren Wang, Xi-Gong Zhang, Ning Shen
Qiu-Yu Li, Zi-Han Pan, Ning Shen, Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing 100191, China
Zhuo-Yu An, Yi-Ren Wang, Department of Education, Peking University People’s Hospital, Beijing 100044, China
Zi-Zhen Wang, Department of Education, China-Japan Friendship Hospital, Beijing 100029, China
Xi-Gong Zhang, Department of Education, Beijing Jishuitan Hospital, Beijing 100096, China
Author contributions: Li QY and An ZY reviewed the literature and contributed to manuscript drafting and revising, both contributed equally to this manuscript, and considered as co-first authors; Pan ZH, Wang ZZ, Wang YR, Zhang XG, Shen N contributed to making a revision to the manuscript; Li QY also contributed to conceptualization, methodology, and funding acquisition; Li QY, An ZY and Zhang XG contributed equally to this paper; all authors issued final approval for the version to be submitted.
Supported byNational Natural Science Foundation of China, No. 81900641; and the Research Funding of Peking University, BMU2021MX020 and BMU2022MX008.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of Peking University Third Hospital (IRB00006761-M2020054 and IRB00006761-M2020055).
Conflict-of-interest statement: The authors declare that they have no conflicts of interest with the contents of this article.
Data sharing statement: No additional data are available.
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/
Corresponding author: Ning Shen, MD, Chief Doctor, Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, No. 49 Huayuan North Road, Haidian District, Beijing 100191, China. shenning1972@126.com
Received: November 1, 2022 Peer-review started: November 1, 2022 First decision: January 30, 2023 Revised: February 12, 2023 Accepted: March 17, 2023 Article in press: March 17, 2023 Published online: April 26, 2023 Processing time: 171 Days and 22.5 Hours
ARTICLE HIGHLIGHTS
Research background
coronavirus disease 2019 (COVID-19) is a global pandemic that requires early identification and intervention to reduce morbidity and mortality. Chest computed tomography (CT) score has been shown to be a factor in the diagnosis and treatment of COVID-19 pneumonia. However, there is currently a lack of effective early warning systems for severe/critical COVID-19.
Research motivation
To develop a severe/critical COVID-19 prediction model using a combination of imaging scores, clinical features, and biomarker levels.
Research objectives
To identify key factors in predicting severe/critical COVID-19 cases using improved chest CT scores and machine learning algorithms.
Research methods
The study used an improved scoring system to extract chest CT characteristics of COVID-19 patients, and considered general clinical indicators such as dyspnea, oxygen saturation, alanine aminotransferase, and aspartate aminotransferase. Lasso regression was employed to evaluate the significance of different disease characteristics.
Research results
A COVID-19 severe/critical early warning system was established using machine learning algorithms including XGBClassifier, Logistic Regression, MLPClassifier, RandomForestClassifier, and AdaBoost Classifier.
Research conclusions
The prediction model based on improved CT scores and machine learning algorithms is effective in detecting early warning signals of severe/critical COVID-19.
Research perspectives
The findings suggest that this method is a feasible solution for early detection of severe/critical COVID-19 evolution and may help reduce morbidity and mortality.
