Machine learning in heart failure: Ready for prime time
© 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 stu...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/70912 |
| _version_ | 1848762338229878784 |
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| author | Awan, S. Sohel, F. Sanfilippo, F. Bennamoun, M. Dwivedi, Girish |
| author_facet | Awan, S. Sohel, F. Sanfilippo, F. Bennamoun, M. Dwivedi, Girish |
| author_sort | Awan, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T10:45:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-70912 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:45:59Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-709122018-12-13T09:33:39Z Machine learning in heart failure: Ready for prime time Awan, S. Sohel, F. Sanfilippo, F. Bennamoun, M. Dwivedi, Girish © 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. 2018 Journal Article http://hdl.handle.net/20.500.11937/70912 10.1097/HCO.0000000000000491 restricted |
| spellingShingle | Awan, S. Sohel, F. Sanfilippo, F. Bennamoun, M. Dwivedi, Girish Machine learning in heart failure: Ready for prime time |
| title | Machine learning in heart failure: Ready for prime time |
| title_full | Machine learning in heart failure: Ready for prime time |
| title_fullStr | Machine learning in heart failure: Ready for prime time |
| title_full_unstemmed | Machine learning in heart failure: Ready for prime time |
| title_short | Machine learning in heart failure: Ready for prime time |
| title_sort | machine learning in heart failure: ready for prime time |
| url | http://hdl.handle.net/20.500.11937/70912 |