Machine learning in heart failure: ready for prime time

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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.

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