Peripheral Artery Disease and CLTI Risk Modeling
Machine learning–based risk stratification in peripheral artery disease under limited data
Abstract
An ongoing study investigating machine learning methods for predicting disease severity and adverse outcomes in patients with peripheral artery disease (PAD), using high-dimensional clinical features from small cohorts, with a focus on clinically meaningful endpoints such as progression to critical limb-threatening ischemia (CLTI) or major amputation.