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 machine learning model based on electronic health record data can provide updated predictions of preeclampsia risk, ...
A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a ...
The frequency of substance use, early age of initiation, and cannabis-related memory impairments are among the primary ...
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 ...
Machine learning predicts who will decline faster in Alzheimer’s disease using routine clinic data
Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...
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.
Researchers develop a radiomics-based machine learning model to identify patients with traumatic brain injury at risk ...
A machine learning model for prediction of preeclampsia risk using routinely collected data was feasible among pregnancies in ...
Discover how a new AI system is revolutionizing energy management by merging machine learning and mathematical programming. This innovative approach not only boosts prediction accuracy but also ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results