Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction

Heart Fail Clin. 2022 Apr;18(2):287-300. doi: 10.1016/j.hfc.2021.12.002. Epub 2022 Mar 4.


Heart failure with preserved ejection fraction (HFpEF) represents a prototypical cardiovascular condition in which machine learning may improve targeted therapies and mechanistic understanding of pathogenesis. Machine learning, which involves algorithms that learn from data, has the potential to guide precision medicine approaches for complex clinical syndromes such as HFpEF. It is therefore important to understand the potential utility and common pitfalls of machine learning so that it can be applied and interpreted appropriately. Although machine learning holds considerable promise for HFpEF, it is subject to several potential pitfalls, which are important factors to consider when interpreting machine learning studies.

Keywords: Artificial intelligence; Deep learning; Heart failure; Machine learning; Natural language processing.

Publication types

  • Review

MeSH terms

  • Heart Failure* / drug therapy
  • Heart Failure* / therapy
  • Humans
  • Machine Learning
  • Precision Medicine
  • Stroke Volume
  • Ventricular Function, Left