Background Deep learning offers considerable promise for medical diagnostics. We aimed
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …

Our primary objective is to provide the clinical informatics community with an introductory
tutorial on calibration measurements and calibration models for predictive models using …

Clinical prediction models are used frequently in clinical practice to identify patients who are
at risk of developing an adverse outcome so that preventive measures can be initiated. A …

Following acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-
2) a significant proportion of individuals develop prolonged symptoms, a serious condition …

Abstract Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in
oncology. Recently, several machine learning (ML) techniques have been adapted for this …

Background Preoperative and perioperative aromatase inhibitor (POAI) therapy has the
potential to improve outcomes in women with operable oestrogen receptor-positive primary …

Binary logistic regression is one of the most frequently applied statistical approaches for
developing clinical prediction models. Developers of such models often rely on an Events …

Background Medical practitioners use survival models to explore and understand the
relationships between patients' covariates (eg clinical and genetic features) and the …

BACKGROUND: It is unclear whether seasonal changes, school closures or other public
health interventions will result in a slowdown of the current coronavirus disease 2019 …

Survival analysis is a subfield of statistics where the goal is to analyze and model data
where the outcome is the time until an event of interest occurs. One of the main challenges …