Introduction. Coronary artery disease (CAD) is one of the main causes of death all over the
world. One way to reduce the mortality rate from CAD is to predict its risk and take effective …

Abstract Machine learning (ML) has been increasingly used within cardiology, particularly in
the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML …

Background Cardiovascular diseases kill approximately 17 million people globally every
year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) …

With the boom in Internet techniques and computer science, a variety of big data have been
introduced into forecasting research, bringing new knowledge and improving prediction …

Background Machine learning (ML) is increasingly used in research for subtype definition
and risk prediction, particularly in cardiovascular diseases. No existing ML models are …

Background This comprehensive systematic review and meta-analysis investigated the mid-
to long-term efficacy and safety of stem cell therapy in patients with acute myocardial …

Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated
with substantial economic burden on healthcare systems around the world. Coronary artery …

Risk stratification of young patients with hypertension remains challenging. Generally,
machine learning (ML) is considered a promising alternative to traditional methods for …

Background Traditional risk assessment tools often lack accuracy when predicting the short-
and long-term mortality following a non-ST-segment elevation myocardial infarction …

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