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.
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.
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 ...
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 ...
Researchers develop a radiomics-based machine learning model to identify patients with traumatic brain injury at 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, ...
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Model could sound the alarm on preeclampsia risk in late pregnancy
How predictions would impact clinical decision-making is another question, expert says ...
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 ...
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