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Machine learning in heart failure

Awan, S.E., Sohel, F., Sanfilippo, F.M., Bennamoun, M. and Dwivedi, G. (2017) Machine learning in heart failure. Current Opinion in Cardiology, 33 (2). pp. 190-195.

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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.

Item Type: Journal Article
Murdoch Affiliation(s): School of Engineering and Information Technology
Publisher: Wolters Kluwer
Copyright: (C) 2018 Wolters Kluwer Health, Inc
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