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Fusing continuous-valued medical labels using a Bayesian Model

Abstract:

With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels include interventions, changes in activity (e.g. sleep) and changes in physiology (e.g. arrhythmias). However, automated algorithms tend to be unreliable resulting in lower quality care. Expert annotations are scarce, expensive, and prone to significant inter- and intra-observer variance. To address these problems, a...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Publisher copy:
10.1007/s10439-015-1344-1

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
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Funding agency for:
Zhu, T
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Funding agency for:
Dunkley, N
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Publisher:
Springer Verlag Publisher's website
Journal:
Annals of Biomedical Engineering Journal website
Volume:
43
Issue:
12
Pages:
2892-2902
Publication date:
2015-06-03
DOI:
EISSN:
1573-9686
ISSN:
0090-6964
URN:
uuid:8ce2bab5-406b-4cf5-b28e-01d64a85ec83
Source identifiers:
527084
Local pid:
pubs:527084

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