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.

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.

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

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

Artificial intelligence tools are increasingly being developed to predict cancer biology directly from microscope images, ...

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 ...