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

Olier, I, Ortega-Martorell, S, Pieroni, M and Lip, GYH How machine learning is impacting research in atrial fibrillation: Implications for risk prediction and future management. Cardiovascular Research. ISSN 0008-6363 (Accepted)

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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 centered on applying ML for detecting 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 modeling; 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 centered 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
Additional Information: This is a pre-copyedited, author-produced version of an article accepted for publication in Cardiovascular Research following peer review. The version of record Ivan Olier, Sandra Ortega-Martorell, Mark Pieroni, Gregory Y H Lip, How machine learning is impacting research in atrial fibrillation: Implications for risk prediction and future management, Cardiovascular Research, 2021;, cvab169 is available online at: https://doi.org/10.1093/cvr/cvab169
Uncontrolled Keywords: 1102 Cardiorespiratory Medicine and Haematology
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Computer Science & Mathematics
Nursing & Allied Health
Publisher: Oxford University Press (OUP)
Date Deposited: 17 May 2021 10:53
Last Modified: 17 May 2021 11:00
DOI or Identification number: 10.1093/cvr/cvab169
URI: https://researchonline.ljmu.ac.uk/id/eprint/15006

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