COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm.

ABSTRACT:

Background

Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters.

Results

Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training ("training set"), while patients admitted after the 5th of March 2021 were used for external validation ("test

SUBMITTER: Lorenzoni G 

PROVIDER: S-EPMC8413709 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature