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Multi-task Gaussian process models for biomedical applications

Abstract:

Gaussian process (GP) models are a flexible means of performing non-parametric Bayesian regression. However, the majority of existing work using GP models in healthcare data is defined for univariate output time-series, denoted as single-task GPs (STGP). Here, we investigate how GPs could be used to model multiple correlated univariate physiological time-series simultaneously. The resulting multi-task GP (MTGP) framework can learn the correlation within multiple signals even though they might...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/BHI.2014.6864410

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Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Sub department:
Centre for Statistics in Medicine
Role:
Author
Royal Academy of Engineering Research Fellowship More from this funder
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Pages:
492-495
Host title:
2014 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2014
Publication date:
2014-01-01
DOI:
Source identifiers:
484271
ISBN:
9781479921317
Pubs id:
pubs:484271
UUID:
uuid:22f72d57-8104-4ca7-ade9-54970b50c133
Local pid:
pubs:484271
Deposit date:
2016-01-18

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