Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by
the American Heart Association in response to the high prevalence of metabolic and kidney …

The deployment of machine learning for tasks relevant to complementing standard of care
and advancing tools for precision health has gained much attention in the clinical …

An accurate prediction of major adverse events after percutaneous coronary intervention
(PCI) improves clinical decisions and specific interventions. To determine whether machine …

Objectives Nearly 20% of pregnancies in patients with SLE result in an adverse pregnancy
outcome (APO). We previously developed an APO prediction model using logistic …

Electronic Health Record (EHR) systems provide critical, rich and valuable information at
high frequency. One of the most exciting applications of EHR data is in developing a real …

Objective Going extended periods of time without eating increases risk for binge eating and
is a primary target of leading interventions for binge‐spectrum eating disorders (B‐EDs) …

In 2011, a multicenter group spearheaded at the University of Virginia demonstrated
reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU …

Machine learning methods have been a standard approach to select features that are
associated with an outcome and to build a prediction model when the number of candidate …

Machine learning (ML) methods are increasingly used to assess variable importance, but
such black box models lack stability when limited in sample sizes, and do not formally …

To seek new signatures of illness in heart rate and oxygen saturation vital signs from
Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time …