Interpretable machine learning for inpatient COVID-19 mortality risk assessments: diabetes mellitus exclusive interplay

Khadem, H. orcid.org/0000-0002-6878-875X, Nemat, H., Elliott, J. et al. (1 more author) (2022) Interpretable machine learning for inpatient COVID-19 mortality risk assessments: diabetes mellitus exclusive interplay. Sensors, 22 (22). 8757. ISSN 1424-8220

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Copyright, Publisher and Additional Information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (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; diabetes mellitus; machine learning; SHAP
Dates:
  • Accepted: 11 November 2022
  • Published (online): 12 November 2022
  • Published: 12 November 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 24 Nov 2022 11:17
Last Modified: 14 Dec 2022 11:46
Status: Published
Publisher: MDPI AG
Refereed: Yes
Identification Number: https://doi.org/10.3390/s22228757

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