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Hospital admission location prediction via deep interpretable networks for the year-round improvement of emergency patient care

Abstract:

Objective: This paper presents a deep learning method of predicting where in a hospital emergency patients will be admitted after being triaged in the Emergency Department (ED). Such a prediction will allow for the preparation of bed space in the hospital for timely care and admission of the patient as well as allocation of resource to the relevant departments, including during periods of increased demand arising from seasonal peaks in infections.

Methods:<...>

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/JBHI.2020.2990309

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Oxford college:
Worcester College
Role:
Author
More by this author
Role:
Author
ORCID:
0000-0001-5095-6367
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
ORCID:
0000-0002-1552-5630
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers
Journal:
IEEE Journal of Biomedical and Health Informatics More from this journal
Volume:
25
Issue:
1
Pages:
289-300
Publication date:
2020-05-29
Acceptance date:
2020-04-21
DOI:
EISSN:
2168-2208
ISSN:
2168-2194
Language:
English
Keywords:
Pubs id:
1101220
Local pid:
pubs:1101220
Deposit date:
2020-04-23

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