Machine learning: at the heart of failure diagnosis

Purpose of review Refinement in machine learning (ML) techniques and approaches has rapidly expanded artificial intelligence applications for the diagnosis and classification of heart failure (HF). This review is designed to provide the clinician with the basics of ML, as well as this technologies future utility in HF diagnosis and the potential impact on patient outcomes. Recent findings Recent studies applying ML methods to unique data sets available from electrocardiography, vectorcardiography, echocardiography, and electronic health records show significant promise for improving diagnosis, enhancing detection, and advancing treatment of HF. Innovations in both supervised and unsupervised methods have heightened the diagnostic accuracy of models developed to identify the presence of HF and further augmentation of model capabilities are likely utilizing ensembles of ML algorithms derived from different techniques. This article is an overview of recent applications of ML to achieve improved diagnosis of HF and the resultant implications for patient management.

Automated summary

The rapid advancement in machine learning techniques has significantly improved the diagnosis and management of heart failure, offering promising prospects for patient outcomes.