Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability
remains controversial. We performed a systematic review to summarize and critically …

Aim To perform a systematic review on the use of Artificial Intelligence (AI) techniques for
predicting COVID-19 hospitalization and mortality using primary and secondary data …

Background The purpose of this paper was to systematically evaluate the application value
of artificial intelligence in predicting mortality among COVID-19 patients. Methods The …

Background: The COVID-19 pandemic affected healthcare systems worldwide. Predictive
models developed by Artificial Intelligence (AI) and based on timely, centralized and …

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

Abstract Machine learning algorithms are being increasingly used in healthcare settings but
their generalizability between different regions is still unknown. This study aims to identify …

Aim. The first prototype of the “Multidisciplinary Tumor Board Smart Virtual Assistant” is
presented, aimed to (i) Automated classification of clinical stage starting from different free …

The spectrum of disease severity and the insidiousness of clinical presentation make it
difficult to recognize patients with coronavirus disease 2019 (COVID-19) at higher risk of …

Abstract Coronavirus disease 2019 (COVID‐19) has become a global pandemic and
continues to prevail with multiple rebound waves in many countries. The driving factors for …

Increased values of the FIB-4 index appear to be associated with poor clinical outcomes in
COVID-19 patients. This study aimed to develop and validate predictive mortality models …