This study highlights the potential for using deep learning methods on longitudinal health data from both primary and ...

Researchers conducted a systematic review to assess the risk of bias and applicability of prediction models for fear of recurrence in patients with cancer.

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests. Systems controlled by next-generation computing ...

Machine learning may help predict Fragile X-associated tremor syndrome earlier, enabling planning, monitoring, and timely ...

Researchers have shown how random forest algorithms can be applied to complex ecological models to uncover the mechanisms driving system behavior. By analyzing a stage‑structured consumer‑resource ...

Final random-forest-based models outperformed all publicly available risk scores on internal and external test sets.

Afforestation—establishing forests on previously non-forested land, or where forests have not existed for a long time—is one ...

Two complementary predictors (DAAE-M and ELIE) estimate individualized 5-year progression risk using routine clinical data, ...

At HRS 2026, Dr. Song Zuo presented evidence that AI can detect atrial fibrillation with over 90% sensitivity, ...

A model integrating deep learning with clinical and epidemiologic data may significantly improve lung cancer risk prediction based on LDCT screening.

Scientists at the European Centre for Medium-Range Weather Forecasts have unveiled a machine learning technique that pinpoints optimal locations for tree planting, offering a powerful tool for climate ...

An artificial intelligence (AI) model developed by researchers at The University of Texas MD Anderson Cancer Center ...