Prognostic models that aim to improve the prediction of clinical events, individualized
treatment and decision-making are increasingly being developed and published. However …

Objective: Electronic health records (EHRs) are an increasingly common data source for
clinical risk prediction, presenting both unique analytic opportunities and challenges. We …

It is widely hoped that statistical models can improve decision-making related to medical
treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically …

Importance The incremental value of polygenic risk scores in addition to well-established
risk prediction models for coronary artery disease (CAD) is uncertain. Objective To examine …

Machine learning, artificial intelligence, and other modern statistical methods are providing
new opportunities to operationalise previously untapped and rapidly growing sources of …

Another recent debate was on the interpretation of the lack of external validation studies of
published novel prediction models [3][4][5]. One issue is the role that validation should have …

After developing a prognostic model, it is essential to evaluate the performance of the model
in samples independent from those used to develop the model, which is often referred to as …

Objectives Predictive analytics in emergency care has mostly been limited to the use of
clinical decision rules (CDR s) in the form of simple heuristics and scoring systems. In the …

Recent decades have seen dramatic progress in brain research. These advances were
often buttressed by probing single variables to make circumscribed discoveries, typically …

Clinical prediction rules (CPRs) that predict the absolute risk of a clinical condition or future
outcome for individual patients are abundant in the medical literature; however, systematic …