Developing and evaluating statistical prediction models is challenging, and many pitfalls can
arise. This article identifies what the authors feel are some common methodological …

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19)
has imposed huge challenges on the healthcare facilities, and impacted every aspect of life …

Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly
from data. This approach has achieved impressive results and has contributed significantly …

Globally, over 17 million people annually die from cardiovascular diseases, with heart
disease being the leading cause of mortality in the United States. The ever-increasing …

With most technical fields, there exists a delay between fundamental academic research and
practical industrial uptake. Whilst some sciences have robust and well-established …

Aim It has been suggested that single neurocognitive domain or neurocognitive test can be
used to determine the overall cognitive function in schizophrenia using machine learning …

There is growing interest in data science and the challenges that scientists can solve
through its application. The growing interest is in part due to the promise of “extracting value …

Ensemble machine learning is the concept of using multiple models to gain better
performance from the combination of weak individual models. New researchers focus on …

Objectives We have seen an increasing number of studies evaluating biomarkers and
prognostic factors. Biomedical researchers like to draw conclusions based on P-values …

Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting
30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were …