Machine learning in heart failure: Ready for prime time
MetadataShow full item record
© 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.
Showing items related by title, author, creator and subject.
Woods, John; Katzenellenbogen, Judith; Davidson, Patricia; Thompson, Sandra (2012)Background: Cardiovascular diseases contribute substantially to the poor health and reduced life expectancy of Indigenous Australians. Heart failure is a common, disabling, progressive and costly complication of these ...
Du, HuiYun (2011)Chronic heart failure is a complex and multifaceted clinical syndrome and impacts adversely on health related quality of life and also increases the risk of hospitalisation and major acute coronary events. Self-care in ...
Machine Learning Based on Computed Tomography Pulmonary Angiography in Evaluating Pulmonary Artery Pressure in Patients with Pulmonary HypertensionZhang, N.; Zhao, X.; Li, J.; Huang, L.; Li, H.; Feng, H.; Garcia, M.A.; Cao, Y.; Sun, Zhonghua ; Chai, S. (2023)Background: Right heart catheterization is the gold standard for evaluating hemodynamic parameters of pulmonary circulation, especially pulmonary artery pressure (PAP) for diagnosis of pulmonary hypertension (PH). However, ...