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

Results 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48
patients were excluded, and the cohort was randomly divided into training (760) and test …

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 The coronavirus disease (COVID-19) hospitalized patients are always at risk of
death. Machine learning (ML) algorithms can be used as a potential solution for predicting …

Background During a pandemic, it is important for clinicians to stratify patients and decide
who receives limited medical resources. Machine learning models have been proposed to …

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

Backgrounds Validated tools for predicting individual in-hospital mortality of COVID-19 are
lacking. We aimed to develop and to validate a simple clinical prediction rule for early …

The sudden increase of COVID-19 cases is putting a high pressure on healthcare services
worldwide. At the current stage, fast, accurate and early clinical assessment of the disease …

Abstract New York City quickly became an epicentre of the COVID-19 pandemic. An ability
to triage patients was needed due to a sudden and massive increase in patients during the …

Objectives To develop and validate a model for prediction of near-term in-hospital mortality
among patients with COVID-19 by application of a machine learning (ML) algorithm on time …