Background: COVID-19 infected millions of people and increased mortality worldwide.
Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended …

Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 …

COVID-19 infection rates remain high in South Africa. Clinical prediction models may be
helpful for rapid triage, and supporting clinical decision making, for patients with suspected …

Background Accurately predicting patient outcomes in Severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare …

Background The nextwave of COVID-19 pandemic is anticipated to be worse than the initial
one and will strain the healthcare systems even more during the winter months. Our aim was …

Background Machine learning (ML) algorithms have been trained to early predict critical in-
hospital events from COVID-19 using patient data at admission, but little is known on how …

Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk
for deterioration during their hospital stay is essential for effective patient allocation and …

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 …

Objective The large number of clinical variables associated with coronavirus disease 2019
(COVID‐19) infection makes it challenging for frontline physicians to effectively triage COVID …

Background The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
outbreak is a public health emergency and the case fatality rate in the United Kingdom is …