Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice
and research. The exponential rise in technology powered by AI is defining new frontiers in …

Recent years have witnessed significant transformations in cardiovascular medicine, driven
by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to …

Abstract Background The triglyceride glucose (TyG) index is a well-established biomarker
for insulin resistance (IR) that shows correlation with poor outcomes in patients with …

Coronary artery disease (CAD) exists on a spectrum of disease represented by a
combination of risk factors and pathogenic processes. An in silico score for CAD built using …

Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left
untreated, yet these patients often endure long diagnostic journeys before being diagnosed …

Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD)
events—the leading cause of global mortality—have known limitations and may be …

Abstract Purpose of Review In this review, we sought to provide an overview of ML and
focus on the contemporary applications of ML in cardiovascular risk prediction and precision …

Aims Existing electronic health records (EHRs) often consist of abundant but irregular
longitudinal measurements of risk factors. In this study, we aim to leverage such data to …

Background Lyme disease is the most prevalent vector-borne disease in the US, yet its host
factors are poorly understood and diagnostic tests are limited. We evaluated patients in a …

Cardiovascular disease holds the position of being the foremost cause of death worldwide.
Heart Disease Prediction (HDP) is a difficult task as it needs advanced knowledge with …