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 ...
COMET, a novel machine learning framework, integrates EHR data and omics analyses using transfer learning, significantly enhancing predictive modeling and uncovering biological insights from small ...
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 for prediction of preeclampsia risk using routinely collected data was feasible among pregnancies in ...
WPI researchers have used a form of artificial intelligence (AI) to analyze anatomical changes in the brain and predict ...
Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a ...
More than half of transplant recipients in a large analysis developed chronic graft-versus-host disease, and 15% died from causes other than cancer relapse. Those numbers capture the uneasy truth of ...
Using machine learning, an electronic nose can "smell" early signs of ovarian cancer in the blood. The method is precise and, ...
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