The team utilized machine learning to analyze public data from the National Health and Nutrition Examination Survey.
Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...
Data is fundamental to hydrological modeling and water resource management; however, it remains a major challenge in many ...
A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
The application of Cox proportional hazards (CoxPH) models to survival data and the derivation of hazard ratio (HR) are well established. Although nonlinear, tree-based machine learning (ML) models ...
Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...
Models using established cardiovascular disease risk factors had satisfactory predictive performance for 5-year CVD risk in ...
Investors make predictable mistakes when forecasting earnings and stock returns, but machine learning models avoid them through adaptive learning.
Forbes contributors publish independent expert analyses and insights. Writes about the future of finance and technology, follow for more. We live in a world where machines can understand speech, ...
Estimating the location of contact is a primary function of artificial tactile sensing apparatuses that perceive the environment through touch. Existing contact localization methods use flat geometry ...
Results that may be inaccessible to you are currently showing.
Hide inaccessible results