How will machine learning inform the clinical care of atrial fibrillation?
Open accessAbstract
Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, is undergoing a knowledge and practice transformation in the increasingly complex healthcare environment. Among other advances, deep-learning machine learning methods, including convolutional neural networks, have enabled the development of AF screening pathways using the ubiquitous 12-lead ECG to detect asymptomatic paroxysmal AF in at-risk populations (such as those with cryptogenic stroke), the refinement of AF and stroke prediction schemes through comprehensive digital phenotyping using structured and unstructured data abstraction from the electronic health record or wearable monitoring technologies, and the optimization of treatment strategies, ranging from stroke prophylaxis to monitoring of antiarrhythmic drug (AAD) therapy. Although the clinical and population-wide impact of these tools continues to be elucidated, such transformative progress does not come without challenges, such as the concerns about adopting black box technologies, assessing input data quality for training such models, and the risk of perpetuating rather than alleviating health disparities. This review critically appraises the advances of machine learning related to the care of AF thus far, their potential future directions, and its potential limitations and challenges.
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ML tools for optimizing care outcomes have been used in critical care for optimizing sepsis management, management of chronic conditions, and optimizing surgical outcomes.
“ML tools for optimizing care outcomes have been used in various settings, including critical care for optimizing sepsis management (27), management of chronic conditions (28), and optimizing surgical outcomes (29).”
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Accessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326 · see citing works
- Authors
- Siontis, K. C., Yao, X., Pirruccello, J. P., Philippakis, A. A., & Noseworthy, P. A.
- Affiliations
- Mayo Clinic (US)
- Broad Institute (US)
- ORCID
- Venue
- Circulation Research
- Published
- 2020-06-19
- Volume
- 127
- Issue
- 1
- Pages
- 155-169
- ISSN
- 0009-7330 (ISSN-L)
- 1524-4571
- OpenAlex
- W3036926534
- Cited by
- 61
- References
- 120
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 18 · 2023: 6 · 2022: 11 · 2021: 9 · 2020: 5
- FWCI
- 1.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Circulation Research (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- atrial fibrillation, machine learning, deep learning, convolutional neural networks, AF screening, 12-lead ECG, cryptogenic stroke, stroke risk prediction, digital phenotyping, electronic health records, wearable monitoring, antiarrhythmic therapy, stroke prevention, health disparities, model interpretability, data quality
- MeSH terms
- Machine Learning, Atrial Fibrillation, Electrocardiography, Humans
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