How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.

ABSTRACT: There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centre

SUBMITTER: Olier I 

PROVIDER: S-EPMC8477792 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature