A novel machine learning model accurately screened for psychologic distress in patients with chronic rhinosinusitis using routine clinical variables.

Utilizing machine learning to assess distinct depressive symptoms improves the identification of adults with suicidal ideation.

Moxank Patel, Machine Learning Engineer at Meta, has worked across machine learning, information retrieval, NLP, computer vision, backend engineering and produc ...

Ishaan Dokania, a sixth-grader from Oregon, is exploring lithium resource identification using satellite imagery and machine ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

A machine learning model utilizing longitudinal electronic diary data can accurately forecast the likelihood of next-day migraine attacks.

ARLINGTON, Va. – U.S. military researchers are approaching industry for new ways of modeling complex, dynamic systems for predicting collective human behavior that overcome challenges that so far have ...

Experiments and machine learning reveal that grain boundary sliding governs the exceptional room-temperature ductility of an ...

A new review in the Journal of Materials Science maps how machine learning, from graph neural networks to large language models, is accelerating the design of high-entropy alloy catalysts across vast ...