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Unsupervised Bayesian inference to fuse biosignal sensory estimates for personalising care

Abstract:

With the increase in volume of wearable sensors, there exists the possibility of personalising patient care, employing automated algorithms. However, automated algorithms are typically less reliable than gold-standard expert labels; the latter are scarce and expensive. In real-life applications, expert labels are not available, and algorithms for processing sensor data must be relied upon, without access to the “ground truth”. It is therefore difficult to choose which algorithms to trust or d...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1109/JBHI.2018.2820054

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
Publisher:
IEEE Publisher's website
Journal:
IEEE Journal of Biomedical and Health Informatics Journal website
Volume:
23
Issue:
1
Pages:
47-58
Publication date:
2018-06-05
Acceptance date:
2018-03-20
DOI:
EISSN:
2168-2208
ISSN:
2168-2194
Pubs id:
pubs:830534
URN:
uri:45eb49e8-3f3b-4444-ac19-7d896a2afad2
UUID:
uuid:45eb49e8-3f3b-4444-ac19-7d896a2afad2
Local pid:
pubs:830534

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