Embracing Uncertainty Flexibility: Harnessing a Supervised Tree Kernel to Empower Ensemble Modelling for 2D Echocardiography-Based Prediction of Right Ventricular Volume

arXiv:2403.03229v1 Announce Type: cross
Abstract: The right ventricular (RV) function deterioration strongly predicts clinical outcomes in numerous circumstances. To boost the clinical deployment of ensemble regression methods that quantify RV volumes using tabular data from the widely available two-dimensional echocardiography (2DE), we propose to complement the volume predictions with uncertainty scores. To this end, we employ an instance-based method which uses the learned tree structure to identify the nearest training samples to a target instance and then uses a number …

abstract arxiv boost clinical cs.lg data deployment eess.iv ensemble flexibility function kernel math.ap modelling prediction q-bio.to regression tabular tabular data tree type uncertainty