Cardiovascular disease is the leading contributor to years lost due to disability or premature
death among adults. Current efforts focus on risk prediction and risk factor mitigation ‚which …

Objective To assess the methodological quality of studies on prediction models developed
using machine learning techniques across all medical specialties. Design Systematic …

The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such
algorithms may be shaped by various factors such as social determinants of health that can …

Aims The 2019 report from the European Society of Cardiology (ESC) Atlas provides a
contemporary analysis of cardiovascular disease (CVD) statistics across 56 member …

Sedentary behaviour—put simply, too much sitting, as a distinct concept from too little
exercise—is a novel determinant of cardiovascular risk. This definition provides a …

Genome-wide association studies have shown unequivocally that common complex
disorders have a polygenic genetic architecture and have enabled researchers to identify …

Clinical prediction models combine multiple predictors to estimate risk for the presence of a
particular condition (diagnostic models) or the occurrence of a certain event in the future …

Most screening tests for T2DM in use today were developed using multivariate regression
methods that are often further simplified to allow transformation into a scoring formula. The …

Objective To evaluate the performance of machine learning (ML) algorithms and to compare
them with logistic regression for the prediction of risk of cardiovascular diseases (CVDs) …

For more on theTRIPOD statement see https://www. tripodstatement. org technology, and
intelligent monitoring. Behind the digital health revolution are also methodological …