Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...

Integrating male sex, RBBB, and haemoglobin and glucose levels into the HEART score improves its ability to predict significant coronary artery disease on CCTA in the emergency department setting.

Current models of mortality risk after heart failure (HF) rely primarily on cardiac-specific clinical variables and may ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

FIU Researchers are training AI to detect heart conditions, like aortic stenosis and heart failure, by analyzing heart sound data to improve early diagnosis and risk prediction. The future of heart ...

Scientists from Peking University conducts a systematic review of studies on integrating machine learning into statistical methods in disease prediction models. Researchers from Peking University have ...

COMET, a novel machine learning framework, integrates EHR data and omics analyses using transfer learning, significantly enhancing predictive modeling and uncovering biological insights from small ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

Heart specialists at Mayo Clinic today presented new research at the 2026 Society of Thoracic Surgeons Annual Meeting that ...