Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We
hypothesized that machine learning (ML) models could be used to predict risks at different …

Background COVID-19 pandemic has developed rapidly and the ability to stratify the most
vulnerable patients is vital. However, routinely used severity scoring systems are often low …

Background Accurate risk prediction of clinical outcome would usefully inform clinical
decisions and intervention targeting in COVID-19. The aim of this study was to derive and …

Background The 2019 novel coronavirus disease (COVID-19) has created unprecedented
medical challenges. There remains a need for validated risk prediction models to assess …

Objectives To develop a prognostic model to identify and quantify risk factors for mortality
among patients admitted to the hospital with COVID-19. Design Retrospective cohort study …

Objective Our objective is to compare the predictive accuracy of four recently established
outcome models of patients hospitalized with coronavirus disease 2019 (COVID-19) …

Background Despite the necessity, there is no reliable biomarker to predict disease severity
and prognosis of patients with COVID-19. The currently published prediction models are not …

Objectives Predictive algorithms to inform risk management decisions are needed for
patients with COVID-19, although the traditional risk scores have not been adequately …

This study aimed to develop risk scores based on clinical characteristics at presentation to
predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 …

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 …