Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review.

ABSTRACT: Machine learning (ML) algorithms "learn" information directly from data, and their performance improves proportionally with the number of high-quality samples. The aim of our systematic review is to present the state of the art regarding the implementation of ML techniques in the management of heart failure (HF) patients. We manually searched MEDLINE and Cochrane databases as well the reference lists of the relevant review studies and included studies. Our search retrieved 122 relevant studies. These studies mainly refer to (a) the role of ML in the classification of HF patients into distinct categories which may require a different treatment strategy, (b) discrimination of HF patients from the healthy population or other diseases, (c) prediction of HF outcomes, (d) identification of HF pa

SUBMITTER: Bazoukis G 

PROVIDER: S-EPMC7384870 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature