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Monitoring patient vital-sign deterioration trajectories using Bayesian inference

Abstract:
Vital signs recorded at the hospital bedside manually by clinical staff are key indicators of patient physiology and may be used to track patient deterioration. The low frequency of vital-sign observations by clinical staff (every 4, 8 or 12 hours) makes it difficult to determine the underlying distribution for each vital sign. In this paper we demonstrate how a Bayesian approach may be used to estimate the unknown parameters of vital sign data.
Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Department of Engineering Science
Oxford college:
Balliol College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Department of Engineering Science
Oxford college:
St John's College
Role:
Author
Publisher:
IEEE Publisher's website
Journal:
Proceedings of the 6th UKRI PG Conference in Biomedical Engineering and Medical Physics 2011
Pages:
1
Host title:
Proceedings of the 6th UKRI PG Conference in Biomedical Engineering and Medical Physics 2011
Publication date:
2011-08-01
Acceptance date:
2011-08-01
Event location:
Glasgow
Source identifiers:
638176
Keywords:
Pubs id:
pubs:638176
UUID:
uuid:9947c878-1f04-4a64-95f0-a8951433ed99
Local pid:
pubs:638176
Deposit date:
2018-02-01

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