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 ...
Accurate prediction of mud loss volume in drilling operations is a critical challenge in industries such as petroleum engineering and geothermal well construction. Unforeseen mud loss leads to ...
Using machine learning models, researchers at Michigan Medicine have identified a potential way to diagnose amyotrophic lateral sclerosis, or ALS, earlier from a blood sample, a study suggests.
Background: Standard CVD risk calculators assume linear relationships among risk factors. ML methods (gradient boosting, random forests, neural networks, support vector machines) capture nonlinear ...
Abstract: Wi-Fi human sensing has attracted numerous research studies over the past decade. The rapid advancement of machine learning technology further boosts the development of Wi-Fi human sensing.
Every Monday morning, Virginia-based cinema owner Mark O’Meara pores over grosses to see how the newest releases are playing in his area. Last weekend didn’t offer much to celebrate as some ...
Nanotechnology and machine learning are transforming energy systems by enhancing engine efficiency and sustainability. The integration of advanced nanomaterials, such as gold nanoparticles (AuNPs), ...
The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...
Health systems and medical practices are increasingly turning to ambient AI technology to help with burdensome medical documentation. This shift is propelling Abridge's rapid growth. The ...
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