Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

Miles, J. orcid.org/0000-0002-1080-768X, Turner, J., Jacques, R. orcid.org/0000-0001-6710-5403 et al. (2 more authors) (2020) Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review. Diagnostic and Prognostic Research, 4. 16.

Abstract

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Copyright, Publisher and Additional Information: © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Ambulance service; Emergency department; Machine learning; Triage; Patients
Dates:
  • Accepted: 11 September 2020
  • Published (online): 2 October 2020
  • Published: December 2020
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) > ScHARR - Sheffield Centre for Health and Related Research
Depositing User: Symplectic Sheffield
Date Deposited: 13 Oct 2020 15:24
Last Modified: 13 Oct 2020 15:24
Status: Published
Publisher: Springer Science and Business Media LLC
Refereed: Yes
Identification Number: https://doi.org/10.1186/s41512-020-00084-1

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