Objective To review and appraise the validity and usefulness of published and preprint
reports of prediction models for prognosis of patients with covid-19, and for detecting people …

Background Clinical risk scores and machine learning models based on routine laboratory
values could assist in automated early identification of severe acute respiratory syndrome …

Background Outpatient coronavirus disease 2019 (COVID-19) has been insufficiently
characterized. To determine the progression of disease and determinants of hospitalization …

Introduction mHealth refers to digital technologies that, via smartphones, mobile apps and
specialised digital sensors, yield real-time assessments of patient's health status. In the …

Many previous studies claim to have developed machine learning models that diagnose
COVID-19 from blood tests. However, we hypothesize that changes in the underlying …

The strain on healthcare resources brought forth by the recent COVID-19 pandemic has
highlighted the need for efficient resource planning and allocation through the prediction of …

Background Age and disease prevalence are the 2 biggest risk factors for Coronavirus
disease 2019 (COVID-19) symptom severity and death. We therefore hypothesized that …

Background The novel coronavirus pandemic has had a differential impact on communities
of color across the US. The University of California hospital system serves a large population …

Abstract Purpose Acute Kidney Injury (AKI) in COVID-19 patients is associated with
increased morbidity and mortality. In the present study, we aimed to develop a prognostic …

Distinguishing critical laboratory biomarkers for disease severity at the time of hospital
presentation is important for early identification of patients who are most likely to have poor …