Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19 : development, application and comparison of machine learning and deep learning methods

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Hasan, M., Bath, P.A. orcid.org/0000-0002-6310-7396, Marincowitz, C. et al. (12 more authors) (2022) Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19 : development, application and comparison of machine learning and deep learning methods. Computers in Biology and Medicine, 151, Part A. 106024. ISSN 0010-4825

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2022 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: COVID-19; outcomes; support vector machines; extreme gradient boosting; artificial neural networks; stacking ensemble
Dates:
  • Accepted: 20 August 2022
  • Published (online): 28 August 2022
  • Published: December 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > Sheffield Centre for Health and Related Research
The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)
Funding Information:
FunderGrant number
NIHR Evaluation Trials and Studies Coordinating Centre11/46/07
Depositing User: Symplectic Sheffield
Date Deposited: 30 Aug 2022 12:18
Last Modified: 07 Nov 2022 14:15
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.compbiomed.2022.106024

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