Machine learning for decision-making in cardiology: a narrative review to aid navigating the new landscape.

Journal: Revista espanola de cardiologia (English ed.)
Published Date:

Abstract

Machine learning in cardiology is becoming more commonplace in the medical literature; however, machine learning models have yet to result in a widespread change in practice. This is partly due to the language used to describe machine, which is derived from computer science and may be unfamiliar to readers of clinical journals. In this narrative review, we provide some guidance on how to read machine learning journals and additional guidance for investigators considering instigating a study using machine learning. Finally, we illustrate the current state of the art with brief summaries of 5 articles describing models that range from the very simple to the highly sophisticated.

Authors

  • John W Pickering
    Christchurch Heart Institute, Department of Medicine, University of Otago Christchurch, New Zealand; Emergency Care Foundation, Christchurch Hospital, New Zealand. Electronic address: John.Pickering@otago.ac.nz.