An increasing number of COVID‐19 cases worldwide has overwhelmed the healthcare
system. Physicians are struggling to allocate resources and to focus their attention on high …

Background Accurate prediction of prognostic outcomes in patients with COVID-19 could
facilitate clinical decision-making and medical resource allocation. However, little is known …

In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and
developed a predictive model based on Artificial Intelligence (AI) and Machine Learning …

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 …

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 …

Background As the worldwide spread of coronavirus disease 2019 (COVID-19) continues for
a long time, early prediction of the maximum severity is required for effective treatment of …

Background At the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic
increased the hospital burden of COVID-19 caused by the SARS-Cov-2 and became the …

COVID-19 is a disease currently ravaging the world, bringing unprecedented health and
economic challenges to several nations. There are presently close to five million reported …

Rationale: Prognostic tools for aiding in the treatment of hospitalized COVID-19 patients
could help improve outcome by identifying patients at higher or lower risk of severe disease …

We aimed to propose a mortality risk prediction model using on-admission clinical and
laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three …