This is the largest real-world analysis of mycophenolic acid in pediatric lupus nephritis to date, providing a decision-support system to help balance efficacy and safety.

In A Nutshell Researchers used a machine learning model to rank all 50 U.S. states and Washington, D.C. by socioeconomic vulnerability to flu-like illness, finding wide regional variation in risk.

The frequency of substance use, early age of initiation, and cannabis-related memory impairments are among the primary ...

When combined with clinical markers, smartwatch data was able to help detect insulin resistance with nearly 90 percent ...

A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.

A new study led by York University found that not only could machine-learning models accurately pinpoint differences in healthy controls and those living with HIV, but also found outliers in both ...

Worcester Polytechnic Institute (WPI) researchers have used a form of artificial intelligence (AI) to analyze anatomical changes in the brain and predict Alzheimer's disease with nearly 93% accuracy.

A Lightweight Self-Supervised Representation Learning Framework for Depression Risk Profiling from Synthetic Daily Behavioural Trajectories ...

Rapid advances in artificial intelligence, machine learning, and data-driven computational modeling have opened unprecedented opportunities to transform ...

Type 2 diabetes (T2D) and obesity are metabolic conditions with many causes, including overlapping and distinct genetic features. A polygenic risk score (PRS) can capture multiple genetic risk factors ...