As the world produces exabytes of data, there is a growing need to find new methods that
are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant …

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

Financial fraud is escalating as financial services and operations grow. Despite preventive
actions and security measures deployed to mitigate financial fraud, fraudsters are learning …

Proinflammatory biomarkers have been increasingly used in epidemiologic and intervention
studies over the past decades to evaluate and identify an association of systemic …

Background Acute coronary syndromes (ACS) are the leading cause of global death.
Optimizing mortality risk prediction and early identification of high-risk patients is essential …

Background Machine learning (ML) risk prediction models, although much more accurate
than traditional statistical methods, are inconvenient to use in clinical practice due to their …

Introduction. Hematological indices including red cell distribution width and neutrophil to
lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome …

Myocardial infarction (MI) is the leading cause of human death globally. It occurs when a
blockage in an artery prevents blood and oxygen from reaching the heart muscle, causing …

Aims Systemic inflammatory response, identified by increased total leucocyte counts, was
shown to be a strong predictor of mortality after transcatheter aortic valve implantation …

Aims Identification of high-risk patients and individualized decision support based on
objective criteria for rapid discharge after transcatheter aortic valve implantation (TAVI) are …