Journal article
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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Authors
Funding
+ Engineering & Physical Sciences Research Council
More from this funder
Grant:
EP/N020774/1
EP/P009824/1
Bibliographic Details
- 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
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1071800
- Local pid:
- pubs:1071800
- Deposit date:
- 2021-11-16
Terms of use
- Copyright holder:
- Hill et al.
- Copyright date:
- 2019
- Rights statement:
- ©2019 Hill et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Licence:
- CC Attribution (CC BY)
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