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Gaussian Processes for Monitoring Patients with Mobile Sensors

Abstract:

As wearable physiological sensors become more common, there is a need for algorithms that can use the resulting waveforms to perform robust data analysis. Existing techniques have failed to penetrate into clinical practice due to their perceived lack of robustness. This presentation will argue that the natural framework for inference with noisy, incomplete data is that of Bayesian Gaussian processes. We describe the use of multi-task algorithms for monitoring patients via wearable sensors. Su...

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Publication status:
Published
Peer review status:
Peer reviewed

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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:
NDORMS
Role:
Author
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Name:
National Institute of Health Research
Funding agency for:
Clifton, L
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Name:
Balliol College, Oxford
Funding agency for:
Clifton, D
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Name:
Royal Academy of Engineering
Funding agency for:
Clifton, D
Publisher:
IEEE
Host title:
37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Series:
Proc. IEEE Engineering in Medicine and Biology Conf., Milan, Italy, 2015
Publication date:
2015-01-01
Pubs id:
pubs:541427
UUID:
uuid:ca54334c-1f98-4c8a-82cd-cd66f040f342
Local pid:
pubs:541427
Source identifiers:
541427
Deposit date:
2015-09-02

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