- Abstract:
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This study examines a large routinely collected healthcare database containing patient-level self-reported outcomes following knee replacement surgery. A model based on unsupervised machine learning methods, including k-means and hierarchical clustering, is proposed to detect patterns of pain experienced by patients and to derive subgroups of patients with different outcomes based on their pain characteristics. Results showed the presence of between two and four different sub-groups of patien...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted manuscript
- Files:
-
-
(pdf, 705.3kb)
-
- Publisher copy:
- 10.5220/0006535602660273
-
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- Publisher:
- Scitepress Publisher's website
- Publication date:
- 2018-02-05
- Acceptance date:
- 2017-10-30
- DOI:
- Pubs id:
-
pubs:742155
- URN:
-
uri:b61e58b5-6b06-41ff-8325-c51d3db107d4
- UUID:
-
uuid:b61e58b5-6b06-41ff-8325-c51d3db107d4
- Local pid:
- pubs:742155
- Copyright holder:
- Science and Technology Publications, Lda.
- Copyright date:
- 2018
- Notes:
- Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Conference item
An unsupervised learning model for pattern recognition in routinely collected healthcare data
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National Institute for Health Research
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