A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.

WPI researchers use machine learning and brain scans to identify age- and sex-specific anatomical patterns that predict ...

Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

A new computational method allows modern atomic models to learn from experimental thermodynamic data, according to a ...

WPI researchers have used a form of artificial intelligence (AI) to analyze anatomical changes in the brain and predict ...

Artificial intelligence tools are increasingly being developed to predict cancer biology directly from microscope images, ...

A research team funded by the National Institutes of Health (NIH) has developed a versatile machine learning model that could one day greatly expand what medical scans can tell us about disease.

The framework predicts how proteins will function with several interacting mutations and finds combinations that work well ...

University of Warwick research warns that popular deep learning systems trained for cancer pathology may be relying on hidden ...

A machine learning model predicted cardiac tamponade during AF ablation with high accuracy. Learn how XGBoost may improve risk stratification.

This research initiative highlights the importance of ethical and explainable artificial intelligence in workforce ...