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.

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

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

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 research team from Juntendo University in Japan wanted to find a better way to predict survival for older people with heart failure. The project was led by Professor Tetsuya Takahashi, Assistant ...

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