Background The application of machine learning (ML) to analyze clinical data with the goal
to predict patient outcomes has garnered increasing attention. Ensemble learning has been …

Objective Risk prediction models are widely used to inform evidence-based clinical decision
making. However, few models developed from single cohorts can perform consistently well …

Background Cardiovascular and other circulatory system diseases have been implicated in
the severity of COVID-19 in adults. This study provides a super learner ensemble of models …

As a result of the increased number of COVID-19 cases, Ensemble Machine Learning (EML)
would be an effective tool for combatting this pandemic outbreak. An ensemble of classifiers …

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 …

Starting from December 2019, the COVID-19 pandemic has globally strained medical
resources and caused significant mortality. It is commonly recognized that the severity of …

Importance Machine learning could be used to predict the likelihood of diagnosis and
severity of illness. Lack of COVID-19 patient data has hindered the data science community …

The recent outbreak of novel coronavirus disease (COVID-19) has resulted in healthcare
crises across the globe. Moreover, the persistent and prolonged complications of post …

Background COVID-19 has infected millions of people worldwide and is responsible for
several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful …

The majority of early prediction scores and methods to predict COVID-19 mortality are bound
by methodological flaws and technological limitations (eg, the use of a single prediction …