tetano
Editor, Senior Moderator
World J Clin Cases
. 2023 Apr 26;11(12):2716-2728.
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[SUP] 1 [/SUP], Zhuo-Yu An[SUP] 2 [/SUP], Zi-Han Pan[SUP] 1 [/SUP], Zi-Zhen Wang[SUP] 3 [/SUP], Yi-Ren Wang[SUP] 2 [/SUP], Xi-Gong Zhang[SUP] 4 [/SUP], Ning Shen[SUP] 5 [/SUP]
Affiliations
Background: Early identification of severe/critical coronavirus disease 2019 (COVID-19) is crucial for timely treatment and intervention. Chest computed tomography (CT) score has been shown to be a significant factor in the diagnosis and treatment of pneumonia, however, there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution.
Aim: To develop a severe/critical COVID-19 prediction model using a combination of imaging scores, clinical features, and biomarker levels.
Methods: This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients. The study also took into consideration the general clinical indicators such as dyspnea, oxygen saturation, alternative lengthening of telomeres (ALT), and androgen suppression treatment (AST), which are commonly associated with severe/critical COVID-19 cases. The study employed lasso regression to evaluate and rank the significance of different disease characteristics.
Results: The results showed that blood oxygen saturation, ALT, IL-6/IL-10, combined score, ground glass opacity score, age, crazy paving mode score, qsofa, AST, and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases. The study established a COVID-19 severe/critical early warning system using various machine learning algorithms, including XGBClassifier, Logistic Regression, MLPClassifier, RandomForestClassifier, and AdaBoost Classifier. The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution.
Conclusion: The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.
Keywords: COVID-19; Clinical prediction model; Electron computed tomography; Machine learning.
. 2023 Apr 26;11(12):2716-2728.
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[SUP] 1 [/SUP], Zhuo-Yu An[SUP] 2 [/SUP], Zi-Han Pan[SUP] 1 [/SUP], Zi-Zhen Wang[SUP] 3 [/SUP], Yi-Ren Wang[SUP] 2 [/SUP], Xi-Gong Zhang[SUP] 4 [/SUP], Ning Shen[SUP] 5 [/SUP]
Affiliations
- PMID: 37214568
- PMCID: PMC10198108
- DOI: 10.12998/wjcc.v11.i12.2716
Background: Early identification of severe/critical coronavirus disease 2019 (COVID-19) is crucial for timely treatment and intervention. Chest computed tomography (CT) score has been shown to be a significant factor in the diagnosis and treatment of pneumonia, however, there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution.
Aim: To develop a severe/critical COVID-19 prediction model using a combination of imaging scores, clinical features, and biomarker levels.
Methods: This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients. The study also took into consideration the general clinical indicators such as dyspnea, oxygen saturation, alternative lengthening of telomeres (ALT), and androgen suppression treatment (AST), which are commonly associated with severe/critical COVID-19 cases. The study employed lasso regression to evaluate and rank the significance of different disease characteristics.
Results: The results showed that blood oxygen saturation, ALT, IL-6/IL-10, combined score, ground glass opacity score, age, crazy paving mode score, qsofa, AST, and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases. The study established a COVID-19 severe/critical early warning system using various machine learning algorithms, including XGBClassifier, Logistic Regression, MLPClassifier, RandomForestClassifier, and AdaBoost Classifier. The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution.
Conclusion: The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.
Keywords: COVID-19; Clinical prediction model; Electron computed tomography; Machine learning.