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.

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

Characterized by weakened or damaged heart musculature, heart failure results in the gradual buildup of fluid in a patient's lungs, legs, feet, and other parts of the body. The condition is chronic ...

A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a ...

Researchers develop a radiomics-based machine learning model to identify patients with traumatic brain injury at risk ...

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.

LCGC International’s interview series on the evolving role of artificial intelligence (AI)/machine learning (ML) in separation science continues with Boudewijn Hollebrands from Unilever Foods R&D, ...

Drug discovery is like molecular Tetris. Chemists snap atoms together, adjusting the pieces until everything fits, and suddenly, a molecule makes a promising new medicine. Normally, creating better ...

Please provide your email address to receive an email when new articles are posted on . Early detection and treatment of sepsis can improve outcomes for children. A team of physicians and computer ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...