Researchers sought to determine an effective approach to predict postembolization fever in patients undergoing TACE.

Interpretable AI model could offer new insights into why medicines cause certain side effects, helping to improve future drug safety predictions.

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

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 ...

An agentic AI tool for battery researchers harnesses data from previous battery designs to predict the cycle life of new ...

The team utilized machine learning to analyze public data from the National Health and Nutrition Examination Survey.

Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has long been slow, expensive, and heavily empirical. Machine learning is now ...

Data is fundamental to hydrological modeling and water resource management; however, it remains a major challenge in many ...

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.