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Probabilistic broken-stick model: a regression algorithm for irregularly sampled data with application to eGFR

Abstract:

In order for clinicians to manage disease progression and make effective decisions about drug dosage, treatment regimens or scheduling follow up appointments, it is necessary to be able to identify both short and long-term trends in repeated biomedical measurements. However, this is complicated by the fact that these measurements are irregularly sampled and influenced by both genuine physiological changes and external factors. In their current forms, existing regression algorithms often do no...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.jbi.2017.10.006

Authors


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Role:
Author
ORCID:
0000-0002-9064-1965
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Role:
Author
ORCID:
0000-0002-5379-580X
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Primary Care Health Sciences
Department:
Unknown
Role:
Author
ORCID:
0000-0002-8553-2641
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Name:
Medical Research Council
Grant:
MR/M023281/1
Publisher:
Elsevier
Journal:
Journal of Biomedical Informatics More from this journal
Volume:
76
Pages:
69-77
Publication date:
2017-10-16
Acceptance date:
2017-10-10
DOI:
EISSN:
1532-0480
ISSN:
1532-0464
Pmid:
29042246
Language:
English
Keywords:
Pubs id:
pubs:1013738
UUID:
uuid:05359a5d-915c-4d29-bbfb-6539edaf0cae
Local pid:
pubs:1013738
Source identifiers:
1013738
Deposit date:
2019-12-10

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