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    Machine learning in heart failure: Ready for prime time

    Access Status
    Fulltext not available
    Authors
    Awan, S.
    Sohel, F.
    Sanfilippo, F.
    Bennamoun, M.
    Dwivedi, Girish
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Awan, 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.
    Source Title
    Current Opinion in Cardiology
    DOI
    10.1097/HCO.0000000000000491
    ISSN
    0268-4705
    School
    School of Pharmacy and Biomedical Sciences
    URI
    http://hdl.handle.net/20.500.11937/70912
    Collection
    • Curtin Research Publications
    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.

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