Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence

Nadarajah, R orcid.org/0000-0001-9895-9356, Wu, J orcid.org/0000-0001-6093-599X, Frangi, AF orcid.org/0000-0002-2675-528X et al. (3 more authors) (2021) Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence. BMJ Open, 11 (11). e052887. ISSN 2044-6055

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Item Type: Article
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© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

Dates:
  • Published: 2 November 2021
  • Published (online): 2 November 2021
  • Accepted: 18 October 2021
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Dentistry (Leeds) > Applied Health and Clinical Translation (Leeds)
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Clinical & Population Science Dept (Leeds)
Funding Information:
Funder
Grant number
British Heart Foundation
FS/20/12/34789
Depositing User: Symplectic Publications
Date Deposited: 25 Oct 2021 13:45
Last Modified: 17 Nov 2021 11:18
Status: Published
Publisher: BMJ Publishing Group
Identification Number: 10.1136/bmjopen-2021-052887
Open Archives Initiative ID (OAI ID):

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