Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care: a diagnostic accuracy study

Savage, R, Messenger, M orcid.org/0000-0002-4975-0158, Neal, RD et al. (12 more authors) (2022) Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care: a diagnostic accuracy study. BMJ Open, 12 (4). e053590. ISSN 2044-6055

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Authors/Creators:
Copyright, Publisher and Additional Information: © Author(s) (or their employer(s)) 2022. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0)
Keywords: Adult; Algorithms; Humans; Machine Learning; Middle Aged; Neoplasms; Primary Health Care; Referral and Consultation; Retrospective Studies
Dates:
  • Accepted: 4 March 2022
  • Published (online): 1 April 2022
  • Published: 1 April 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Dentistry (Leeds) > Dentistry (Leeds)
Funding Information:
FunderGrant number
Cancer Research UKNot Known
Depositing User: Symplectic Publications
Date Deposited: 21 Apr 2022 09:30
Last Modified: 21 Apr 2022 09:30
Status: Published
Publisher: BMJ Publishing Group
Identification Number: https://doi.org/10.1136/bmjopen-2021-053590
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