Machine learning in heart failure

S.E. Awan; F. Sohel; F.M. Sanfilippo; M. Bennamoun; G. Dwivedi

doi:10.1097/HCO.0000000000000491

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Journal article

Peer reviewed

Machine learning in heart failure

S.E. Awan, F. Sohel, F.M. Sanfilippo, M. Bennamoun and G. Dwivedi

Current Opinion in Cardiology, Vol.33(2), pp.190-195

2017

DOI: https://doi.org/10.1097/HCO.0000000000000491

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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.

Details

Title: Machine learning in heart failure
Authors/Creators: S.E. Awan (Author/Creator) - The University of Western Australia
F. Sohel (Author/Creator) - Murdoch University
F.M. Sanfilippo (Author/Creator) - School of Population and Global Health.
M. Bennamoun (Author/Creator) - The University of Western Australia
G. Dwivedi (Author/Creator) - Harry Perkins Institute of Medical Research
Publication Details: Current Opinion in Cardiology, Vol.33(2), pp.190-195
Publisher: Wolters Kluwer
Identifiers: 991005542139607891
Copyright: (C) 2018 Wolters Kluwer Health, Inc
Murdoch Affiliation: School of Engineering and Information Technology
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration
Citation topics: 1 Clinical & Life Sciences; 1.37 Cardiology - General; 1.37.328 Heart Failure Management
Web Of Science research areas: Cardiac & Cardiovascular Systems
ESI research areas: Clinical Medicine