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 developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a ...
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 ...
Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...
A machine learning model for prediction of preeclampsia risk using routinely collected data was feasible among pregnancies in ...
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 ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
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, ...
WPI researchers use machine learning and brain scans to identify age- and sex-specific anatomical patterns that predict ...
A new computational method allows modern atomic models to learn from experimental thermodynamic data, according to a ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results