Machine learning in heart failure
Tools
Tools
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.
*Subscription may be required
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.
| Item Type: | Journal Article |
|---|---|
| Murdoch Affiliation(s): | School of Engineering and Information Technology |
| Publisher: | Wolters Kluwer |
| Copyright: | (C) 2018 Wolters Kluwer Health, Inc |
| URI: | http://researchrepository.murdoch.edu.au/id/eprint/40406 |
 |
Item Control Page |