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Cluster analysis to detect patterns of drug use from routinely collected medical data

Abstract:

Appropriate drug prescription for an increasingly ageing and multi-morbid population can be a challenge for general practitioners. This study uses unsupervised learning methods to identify different types of patient profiles which could inform policymakers and regulators about patterns of drug use, and identify specific clusters of users with unknown drug effects (risk and benefit). Hard and soft clustering methods are proposed to detect patterns of medication use by patients and to estimate ...

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

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Publisher copy:
10.1109/CBMS.2018.00041

Authors


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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS; CSM
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS
ORCID:
0000-0002-3950-6346
NIHR Oxford Biomedical Research Centre More from this funder
Publisher:
IEEE Publisher's website
Volume:
2018-June
Pages:
194-198
Publication date:
2018-07-23
Acceptance date:
2018-04-25
DOI:
ISSN:
1063-7125
Pubs id:
pubs:905259
URN:
uri:696e83b9-3557-47ab-afe9-ed50ef24c0d3
UUID:
uuid:696e83b9-3557-47ab-afe9-ed50ef24c0d3
Local pid:
pubs:905259
ISBN:
9781538660607

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