Deep-learning Survival Analysis For Patients With Calcific Aortic Valve Disease Undergoing Valve Replacement

This article discusses the use of machine learning methods in a clinical study to evaluate the association between CHIP and CAVD in elderly patients undergoing valve replacement. The study used a data set of 165 patients and 18 independent variables to predict survival at 12 months after valve replacement. The study highlights the importance of feature selection and hyperparameter tuning in optimizing ML methods for medical diagnosis.