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Conference item

State-space approximations to Gaussian processes for patient vital-sign monitoring in computationally-constrained clinical environments

Abstract:

Gaussian processes (GPs) define a probability distribution over a space of functions from which a set of observed data are assumed to be generated. When applied to a time-series of patient vital-sign data, GP models (i) can encode prior clinical knowledge of the dynamics of the data; (ii) are patient-specific; and (iii) can be learned in real-time. The clinical value of GPs [1], [2] has been demonstrated by their superior performance in advanced warning of deterioration compared to the curren...

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Peer review status:
Reviewed (other)

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Institution:
University of Oxford
Oxford college:
St Cross College
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Funding agency for:
Colopy, G
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Funding agency for:
Pimentel, M
Grant:
HAVEN project, WT 103703/Z/14/Z
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Funding agency for:
Clifton, D
Grant:
Challenge Award
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Funding agency for:
Clifton, D
Grant:
Challenge Award
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Host title:
MEIbioeng 2016
Publication date:
2016-01-01
Acceptance date:
2016-09-06
Event location:
Keble College Oxford
Source identifiers:
682582
Pubs id:
pubs:682582
UUID:
uuid:f6a9177a-8b8b-45e4-aaa4-574863d0f2da
Local pid:
pubs:682582
Deposit date:
2017-03-01

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