Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial

Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients with reduced left ventricular function and intraventricular conduction delay. However, individual outcomes vary significantly. This study sought to use a machine learning algorithm to develop a model to...

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Bibliographic Details
Published in:	Circulation. Arrhythmia and electrophysiology Vol. 11; no. 1; p. e005499
Main Authors:	Kalscheur, Matthew M, Kipp, Ryan T, Tattersall, Matthew C, Mei, Chaoqun, Buhr, Kevin A, DeMets, David L, Field, Michael E, Eckhardt, Lee L, Page, C David
Format:	Journal Article
Language:	English
Published:	United States 01.01.2018
Subjects:	Aged Algorithms Cardiac Resynchronization Therapy - methods Decision Making Deep Learning Female Heart Conduction System - physiopathology Heart Failure - physiopathology Heart Failure - therapy Humans Machine Learning Male Middle Aged Predictive Value of Tests Stroke Volume - physiology Ventricular Function, Left - physiology algorithms heart failure hospitalization machine learning cardiac resynchronization therapy
ISSN:	1941-3084, 1941-3084
Online Access:	Get more information
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