Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …

The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

Autism is a disability that obstructs the process of a person's development. Autistic
individuals find it extremely difficult to cope with the world's pace, can not communicate …

The prediction of traffic crashes is an essential topic in traffic safety research. Most of the
previous studies conducted experiments on real-time crash prediction of expressways or …

In this paper, we develop methods for estimation and forecasting in large time-varying
parameter vector autoregressive models (TVP-VARs). To overcome computational …

Image-based prediction models for disease detection are sensitive to changes in data
acquisition such as the replacement of scanner hardware or updates to the image …

This paper studies the predictability of cryptocurrency time series. We compare several
alternative univariate and multivariate models for point and density forecasting of four of the …

Clinical prediction models (CPMs) have become fundamental for risk stratification across
healthcare. The CPM pipeline (development, validation, deployment, and impact …

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a
specific population and context. A common approach is to develop a new clinical prediction …

Background Disease populations, clinical practice, and healthcare systems are constantly
evolving. This can result in clinical prediction models quickly becoming outdated and less …