- 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 ...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
-
-
(pdf, 636.1kb)
-
- Publisher copy:
- 10.1109/CBMS.2018.00041
-
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- Funding agency for:
- Prieto-Alhambra, D
- 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
- 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
Conference item
Cluster analysis to detect patterns of drug use from routinely collected medical data
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+ National Institute of Health Research
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