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, ...

NOTE. These are the baseline variables determined at treatment completion and included in the analysis. Abbreviations: CIN, cervical intraepithelial neoplasia; COPD, chronic obstructive pulmonary ...

Performance evaluation of an AI-powered system for clinical trial eligibility using mCODE data standards. ATheNa-Breast: A real-world pilot of an artificial intelligence (AI) chatbot using therapy ...

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

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 ...

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

A new technical paper titled “Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate” was published by researchers at KTH Royal Institute of Technology and ...

Enhanced prediction capability: Machine learning-based system matches and in some cases outperforms traditional forecasting systems, with particular improvements in northern Europe where conventional ...

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

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 ...