Research Area: Machine Learning
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.
Keywords:
Machine Learning
Heart Failure
diagnosis
classification
Deep Learning
Author(s) Name: Awan, Saqib Ejaza; Sohel, Ferdousb; Sanfilippo, Frank Marioc; Bennamoun, Mohammeda; Dwivedi, Girish
Journal name: Current Opinion in Cardiology
Conferrence name:
Publisher name: Lippincott Williams & Wilkins
DOI: 10.1097/HCO.0000000000000491
Volume Information: Volume 33, Issue 2, p 190-195
