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. 2022 Oct;17(7):1929-1939.
doi: 10.1007/s11739-022-03033-6. Epub 2022 Sep 13.

Mortality predictors in patients with COVID-19 pneumonia: a machine learning approach using eXtreme Gradient Boosting model

N Casillas  1   2 , A M Torres  3 , M Moret  4 , A Gómez  4 , J M Rius-Peris  3   5 , J Mateo  3
Affiliations

Mortality predictors in patients with COVID-19 pneumonia: a machine learning approach using eXtreme Gradient Boosting model

N Casillas et al. Intern Emerg Med. 2022 Oct.

Abstract

Recently, global health has seen an increase in demand for assistance as a result of the COVID-19 pandemic. This has prompted many researchers to conduct different studies looking for variables that are associated with increased clinical risk, and find effective and safe treatments. Many of these studies have been limited by presenting small samples and a large data set. Using machine learning (ML) techniques we can detect parameters that help us to improve clinical diagnosis, since they are a system for the detection, prediction and treatment of complex data. ML techniques can be valuable for the study of COVID-19, especially because they can uncover complex patterns in large data sets. This retrospective study of 150 hospitalized adult COVID-19 patients, of which we established two groups, those who died were called Case group (n = 53) while the survivors were Control group (n = 98). For analysis, a supervised learning algorithm eXtreme Gradient Boosting (XGBoost) has been used due to its good response compared to other methods because it is highly efficient, flexible and portable. In this study, the response to different treatments has been evaluated and has made it possible to accurately predict which patients have higher mortality using artificial intelligence, obtaining better results compared to other ML methods.

Keywords: Artificial intelligence; COVID-19; Machine learning; Mortality; Prediction; SARS-CoV-2; XGB.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Training and validation scheme for machine learning methods
Fig. 2
Fig. 2
Mortality predictors in COVID-19. This figure shows the different parameters that have been identified as predictors of mortality. Section A presents the initial laboratory parameters, section B comorbidities, and section C laboratory evolution and complications during hospital admission of COVID-19 patients
Fig. 3
Fig. 3
Section A: ROC curves for the five assessed machine learning predictors. Section B: The figure shows the radar plot of the training phase (left) and test (right) for the prediction of mortality in COVID-19 patients
See this image and copyright information in PMC

Comment in

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