A machine learning-based heart disease prediction model (ML-HDPM) that uses various combinations of information and numerous recognized categorization methods.

Using a cohort of more than 33,000 Chinese patients, investigators comprehensively analyzed urinary stone composition.

Our findings suggest that integrating machine learning into traditional statistical methods can provide more accurate and generalizable models for disease risk prediction.

A machine learning model bests traditional methods for predicting cirrhosis mortality among hospitalized patients.

Dr. Shipra Arya, Stanford vascular surgeon, receives $300,000 AHA award to develop an automated deep learning–based ...

Stanford University researchers developed a machine learning-based method capable of diagnosing multiple diseases using B cell and T cell receptor sequences. The model, called Machine learning for ...

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.

Melkani envisions using deep learning-assisted studies to explore cardiac mutation models and other small animal models, such as zebrafish and mice. “Additionally, our techniques could be adapted for ...

Study published in The Journal of Thoracic and Cardiovascular Surgery indicates OneBreath™ technology can help diagnose and predict pneumonia from a single exhale. "Exhaled breath is an extraordinary ...