Conference item
Cluster analysis to detect patterns of drug use from routinely collected medical data
- Abstract:
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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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- Files:
-
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(Accepted manuscript, pdf, 636.1KB)
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- Publisher copy:
- 10.1109/CBMS.2018.00041
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Authors
Funding
+ National Institute of Health Research
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
Bibliographic Details
- 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:
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1063-7125
- Source identifiers:
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905259
- ISBN:
- 9781538660607
Item Description
- Keywords:
- Pubs id:
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pubs:905259
- UUID:
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uuid:696e83b9-3557-47ab-afe9-ed50ef24c0d3
- Local pid:
- pubs:905259
- Deposit date:
- 2018-08-13
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2018
- Notes:
- Copyright © 2018 IEEE. This is the accepted manuscript version of the paper. The final version is available online from IEEE at: https://doi.org/10.1109/CBMS.2018.00041
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