COVID-19 is responsible for high mortality, but robust machine learning-based predictors of
mortality are lacking. To generate a model for predicting mortality in patients hospitalized …

The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early
and accurately as possible the severity level of the disease in a COVID-19 patient who is …

The present work aims to identify the predictors of COVID-19 in-hospital mortality testing a
set of Machine Learning Techniques (MLTs), comparing their ability to predict the outcome …

Background Several models have been developed to predict mortality in patients with
COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity …

Background Several risk factors have been identified to predict worse outcomes in patients
affected by SARS-CoV-2 infection. Machine learning algorithms represent a novel approach …

Background The COVID-19 pandemic has affected millions of individuals and caused
hundreds of thousands of deaths worldwide. Predicting mortality among patients with COVID …

The response to the ongoing second wave of the COVID-19 pandemic can be helped by
giving medical professionals access to models learned on patient data. To achieve this, we …

Abstract Background Coronavirus disease (COVID-19) presents an unprecedented threat to
global health worldwide. Accurately predicting the mortality risk among the infected …

Objectives External validation of the 4C and NEWS2 scores for the prediction of in-hospital
mortality in COVID-19 patients, and evaluation of its operational performance in two time …

Abstract In Coronavirus disease 2019 (COVID-19), early identification of patients with a high
risk of mortality can significantly improve triage, bed allocation, timely management, and …