Machine learning predicts mortality in septic patients using only routinely available ABG variables: a multi-centre evaluation

Wernly, B., Mamandipoor, B., Baldia, P. et al. (2 more authors) (2021) Machine learning predicts mortality in septic patients using only routinely available ABG variables: a multi-centre evaluation. International Journal of Medical Informatics, 145. 104312. ISSN 1386-5056

Abstract

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Copyright, Publisher and Additional Information: © 2020 Elsevier B.V. This is an author produced version of a paper subsequently published in International Journal of Medical Informatics. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: critically ill; artificial intelligence; machine learning; deep learning; LSTM; ICU; risk stratification; intensive care unit; critical care; sepsis
Dates:
  • Accepted: 20 October 2020
  • Published (online): 24 October 2020
  • Published: January 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 29 Nov 2022 11:06
Last Modified: 29 Nov 2022 15:58
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.ijmedinf.2020.104312
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