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Machine learning in heart failure: ready for prime time

Awan, Saqib, Ejaza; Sohel, Ferdousb; Sanfilippo, Frank, Marioc; Bennamoun, Mohammeda; Dwivedi, Girishd

Current Opinion in Cardiology: March 2018 - Volume 33 - Issue 2 - p 190–195
doi: 10.1097/HCO.0000000000000491
HEART FAILURE: Edited by Haissam Haddad

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.

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: girish.dwivedi@perkins.uwa.edu.au

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