A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification

arXiv:2309.08415v3 Announce Type: replace
Abstract: Aims. The purpose of this study is to create a multi-stage machine learning model to predict cardiac resynchronization therapy (CRT) response for heart failure (HF) patients. This model exploits uncertainty quantification to recommend additional collection of single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) variables if baseline clinical variables and features from electrocardiogram (ECG) are not sufficient. Methods. 218 patients who underwent rest-gated SPECT MPI were enrolled in this study. CRT response was defined …

abstract arxiv collection cs.lg decision eess.sp exploits failure heart failure machine machine learning machine learning model making modeling patients photon physics.med-ph process quantification stage study therapy type uncertainty