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Hill, NR, Groves, L, Dickerson, C, Ochs, A, Lawton, SA ORCID: https://orcid.org/0000-0002-8909-2057, Hurst, M, Pollock, KG, Sugrue, D, Tsang, C, Arden, C, Davies, DW, Martin, AC, Sandler, B, Gordon, J, Farooqui, U, Clifton, D, Mallen, CD
ORCID: https://orcid.org/0000-0002-2677-1028, Rogers, J, Camm, JA and Cohen, A
(2022)
Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomised controlled trial in England.
European Heart Journal, 25 (1).
S111 - S111.
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Text
ztac009.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial. Download (1MB) | Preview |
Official URL: https://academic.oup.com/ehjdh/advance-article/doi...
Item Type: | Article |
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Additional Information: | This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
Uncontrolled Keywords: | atrial fibrillation, machine learning, risk prediction, primary care, screening |
Subjects: | R Medicine > R Medicine (General) |
Divisions: | Faculty of Medicine and Health Sciences > School of Medicine |
Related URLs: | |
Depositing User: | Symplectic |
Date Deposited: | 14 Jun 2022 15:46 |
Last Modified: | 14 Jun 2022 15:46 |
URI: | https://eprints.keele.ac.uk/id/eprint/11005 |
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