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, ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...

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.

Researchers conducted a review of studies on AI use in oncology nursing to determine its potential role in clinical practice.

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Feasibility and Implementation of a Digital Health Intervention Electronic Patient-Reported Outcomes–Based Platform for Telemonitoring Patients With Breast Cancer Undergoing Chemotherapy Among the 76 ...

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 ...