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

Olier, Ivan, Ortega-Martorell, Sandra, Pieroni, Mark and Lip, Gregory YH ORCID: 0000-0002-7566-1626 (2021) How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management. CARDIOVASCULAR RESEARCH, 117 (7). pp. 1700-1717.

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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 centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable 'real time' dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate 'real time' assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.

Item Type:	Article
Uncontrolled Keywords:	Atrial fibrillation, Artificial intelligence, Machine learning, Risk analysis, Wearables
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	16 Dec 2021 15:07
Last Modified:	18 Jan 2023 21:19
DOI:	10.1093/cvr/cvab169
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3145508