HEART FAILURE: Edited by Haissam HaddadMachine learning in heart failure ready for prime timeAwan, Saqib Ejaza; Sohel, Ferdousb; Sanfilippo, Frank Marioc; Bennamoun, Mohammeda; Dwivedi, Girishd Author Information aSchool of Computer Science and Software Engineering, The University of Western Australia bSchool of Engineering and Information technology, Murdoch University cSchool of Population and Global Health dWesfarmers Chair and Consultant Cardiologist, Harry Perkins Institute of Medical Research and Fiona Stanley Hospital, The University of Western Australia, Perth, Australia Correspondence to Professor Girish Dwivedi, MD, PhD, MRCP, FASE, FESC, Wesfarmers Chair in Cardiology and Consultant Cardiologist, Harry Perkins Institute of Medical Research and Fiona Stanley Hospital, The University of Western Australia, Perth, Australia. Tel: +61 8 6151 0000; e-mail: [email protected] Current Opinion in Cardiology: March 2018 - Volume 33 - Issue 2 - p 190-195 doi: 10.1097/HCO.0000000000000491 Buy Metrics Abstract Purpose of review The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent findings Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. Summary The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management. Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.