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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

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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
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Role:
Author
ORCID:
0000-0002-3950-6346
More from this funder
Funding agency for:
Prieto-Alhambra, D
Grant:
Clinician Scientist award (CS-2013-13-012
NIHR Oxford Biomedical Research Centre More from this funder
Publisher:
IEEE Publisher's website
Journal:
2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS) Journal website
Volume:
2018-June
Pages:
194-198
Host title:
2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)
Publication date:
2018-07-23
Acceptance date:
2018-04-25
DOI:
ISSN:
1063-7125
Source identifiers:
905259
ISBN:
9781538660607
Keywords:
Pubs id:
pubs:905259
UUID:
uuid:696e83b9-3557-47ab-afe9-ed50ef24c0d3
Local pid:
pubs:905259
Deposit date:
2018-08-13

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