Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Montomoli, Jonathan; Romeo, Luca; Moccia, Sara; Bernardini, Michele; Migliorelli, Lucia; Berardini, Daniele; Donati, Abele; Carsetti, Andrea; Bocci, Maria Grazia; Wendel Garcia, Pedro David; Fumeaux, Thierry; Guerci, Philippe; Schüpbach, Reto; Ince, Can; Frontoni, Emanuele; Hilty, Matthias Peter

doi:10.1016/j.jointm.2021.09.002

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Montomoli, J., Romeo, L., Moccia, S., Bernardini, M., Migliorelli, L., Berardini, D., Donati, A., Carsetti, A., Bocci, M. G., Wendel Garcia, P. D., Fumeaux, T., Guerci, P., Schüpbach, R., Ince, C., Frontoni, E., & Hilty, M. P. (2021). Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients. Journal of Intensive Medicine, 1(2), 110–116. https://doi.org/10.1016/j.jointm.2021.09.002

Abstract

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) be