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dc.contributor.authorAwan, S.
dc.contributor.authorSohel, F.
dc.contributor.authorSanfilippo, F.
dc.contributor.authorBennamoun, M.
dc.contributor.authorDwivedi, Girish
dc.date.accessioned2018-12-13T09:08:07Z
dc.date.available2018-12-13T09:08:07Z
dc.date.created2018-12-12T02:46:54Z
dc.date.issued2018
dc.identifier.citationAwan, S. and Sohel, F. and Sanfilippo, F. and Bennamoun, M. and Dwivedi, G. 2018. Machine learning in heart failure: Ready for prime time. Current Opinion in Cardiology. 33 (2): pp. 190-195.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/70912
dc.identifier.doi10.1097/HCO.0000000000000491
dc.description.abstract

© 2018 Wolters Kluwer Health, Inc. All rights reserved. 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.

dc.titleMachine learning in heart failure: Ready for prime time
dc.typeJournal Article
dcterms.source.volume33
dcterms.source.number2
dcterms.source.startPage190
dcterms.source.endPage195
dcterms.source.issn0268-4705
dcterms.source.titleCurrent Opinion in Cardiology
curtin.departmentSchool of Pharmacy and Biomedical Sciences
curtin.accessStatusFulltext not available


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