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Personalised patient monitoring in haemodialysis using hierarchical Gaussian processes

Abstract:

The prevalence of end-stage renal failure is 861 per million population in the UK, and these patients undergo three haemodialysis sessions per week, each lasting 4 hours. In addition, patients are at risk of intra-dialytic hypotension, which leads to chronic heart disease and a high incidence of mortality. Through continuous monitoring of blood pressure during dialysis, we describe the use of Gaussian process regression to model changes of systolic blood pressure over time for each patient. W...

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Publication status:
In press
Peer review status:
Reviewed (other)

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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:
MSD
Department:
NDM
Sub department:
Target Discovery Institute
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:
Clifton, D
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Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Journal website
Host title:
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publication date:
2017-05-01
Acceptance date:
2017-05-16
Source identifiers:
697079
Pubs id:
pubs:697079
UUID:
uuid:f9cc27d2-d47a-45f5-8f7a-a3a8f792b303
Local pid:
pubs:697079
Deposit date:
2017-05-25

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