Evaluation of machine learning methods for the retrospective detection of ovarian cancer recurrences from chemotherapy data

Coles, A.D. orcid.org/0000-0002-2657-0090, McInerney, C.D. orcid.org/0000-0001-7620-7110, Zucker, K. et al. (3 more authors) (2024) Evaluation of machine learning methods for the retrospective detection of ovarian cancer recurrences from chemotherapy data. ESMO Real World Data and Digital Oncology, 4. 100038. ISSN 2949-8201

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Item Type: Article
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© 2024 The Authors. Published by Elsevier Ltd on behalf of European Society for Medical Oncology. User License: Creative Commons Attribution (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/

Keywords: cancer recurrence; chemotherapy; electronic health record; machine learning; artificial intelligence
Dates:
  • Published: June 2024
  • Published (online): 30 April 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health
Depositing User: Symplectic Sheffield
Date Deposited: 13 Dec 2024 12:40
Last Modified: 13 Dec 2024 12:40
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: 10.1016/j.esmorw.2024.100038
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