Keele Research Repository

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Wu, J, Nadarajah, R, Nakao, Y, Nakao, K, Wilkinson, C, Raveendra, K, Mamas, MA ORCID: https://orcid.org/0000-0001-9241-8890, Camm, AJ and Gale, CP (2022) Temporal trends and patterns in atrial fibrillation incidence: a population-based study of 3.4 million individuals. European Heart Journal, 43 (Supple).

Hill, NR, Groves, L, Dickerson, C, Boyce, R, Lawton, S, Hurst, M, Pollock, KG, Sugrue, DM, Lister, S, Arden, C, Davies, DW, Martin, A-C, Sandler, B, Gordon, J, Farooqui, U, Clifton, D, Mallen, CD ORCID: https://orcid.org/0000-0002-2677-1028, Rogers, J, Camm, AJ and Cohen, AT (2022) Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England. Journal of Medical Economics, 25 (1). 974 - 983.

Hill, NR, Arden, C, Beresford-Hulme, L, Camm, AJ, Clifton, D, Davies, DW, Farooqui, U, Gordon, J, Groves, L, Hurst, M, Lawton, S, Lister, S, Mallen, C ORCID: https://orcid.org/0000-0002-2677-1028, Martin, A-C, McEwan, P, Pollock, KG, Rogers, J, Sandler, B, Sugrue, DM and Cohen, AT (2020) Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial. Contemporary Clinical Trials Communications, 99. 106191 - ?.