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 ...
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 ...
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 ...
Cancer, Alzheimer’s, and other diseases follow a pathway in the human body. It starts at the molecular and cellular levels, and through a series of complex interactions can lead to the development and ...
Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more ...
More than half of transplant recipients in a large analysis developed chronic graft-versus-host disease, and 15% died from ...
Using machine learning, an electronic nose can "smell" early signs of ovarian cancer in the blood. The method is precise and, ...
Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.
Results that may be inaccessible to you are currently showing.
Hide inaccessible results