Saqib Ejaz Awan1, Ferdous Sohel2, Frank Mario Sanfilippo3, Mohammed Bennamoun1, Girish Dwivedi4. 1. School of Computer Science and Software Engineering, The University of Western Australia. 2. School of Engineering and Information technology, Murdoch University. 3. School of Population and Global Health. 4. Wesfarmers Chair and Consultant Cardiologist, Harry Perkins Institute of Medical Research and Fiona Stanley Hospital, The University of Western Australia, Perth, Australia.
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.
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.
Authors: Bohdan B Khomtchouk; Diem-Trang Tran; Kasra A Vand; Matthew Might; Or Gozani; Themistocles L Assimes Journal: Brief Bioinform Date: 2020-12-01 Impact factor: 11.622
Authors: Aguinaldo F Freitas; Fábio S Silveira; Germano E Conceição-Souza; Manoel F Canesin; Pedro V Schwartzmann; Sabrina Bernardez-Pereira; Reinaldo B Bestetti Journal: Arq Bras Cardiol Date: 2020-12 Impact factor: 2.000