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Predicting atrial fibrillation in primary care using machine learning

Abstract:

Background
Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of the undiagnosed may be supported by risk-prediction models relating patient factors to AF risk. However, there exists a need for an implementable risk model that is contemporaneous and informed by routinely collected patient... Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1371/journal.pone.0224582

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Role:
Author
ORCID:
0000-0001-6352-0394
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Publisher:
Public Library of Science Publisher's website
Journal:
PloS One Journal website
Volume:
14
Issue:
11
Article number:
e0224582
Publication date:
2019-01-01
Acceptance date:
2019-10-16
DOI:
EISSN:
1932-6203
Pmid:
31675367

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