Machine learning in heart failure: Ready for prime time

Research output: Contribution to journalReview article

17 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)190-195
Number of pages6
JournalCurrent Opinion in Cardiology
Volume33
Issue number2
DOIs
Publication statusPublished - 1 Mar 2018

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