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An unsupervised learning model for pattern recognition in routinely collected healthcare data

Abstract:

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...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.5220/0006535602660273

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Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Role:
Author
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS
Role:
Author
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS
Role:
Author
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Name:
National Institute for Health Research
Publisher:
Scitepress
Host title:
11th International Conference on Health Informatics (Healthinf 2018)
Journal:
11th International Conference on Health Informatics (HEALTHINF 2018) More from this journal
Publication date:
2018-02-01
Acceptance date:
2017-10-30
DOI:
Keywords:
Pubs id:
pubs:742155
UUID:
uuid:b61e58b5-6b06-41ff-8325-c51d3db107d4
Local pid:
pubs:742155
Source identifiers:
742155
Deposit date:
2017-11-01

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