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Mining Rare Cases in Post-Operative Pain by Means of Outlier Detection
Mälardalen University, School of Innovation, Design and Engineering. (Intelligent Systems)ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering. (Intelligent Systems)ORCID iD: 0000-0002-5562-1424
2011 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Rare cases are often interesting for healthprofessionals, physicians, researchers and clinicians in order toreuse and disseminate experiences in healthcare. However,mining, i.e. identification of rare cases in electronic patientrecords, is non-trivial for information technology. This paperinvestigates a number of well-known clustering algorithms andfinally applies a 2nd order clustering approach by combining theFuzzy C-means algorithm with the Hierarchical one. Theapproach is used in order to identify rare cases from 1572patient cases in the domain of post-operative pain management.The results show that the approach enables identification of rarecases in the domain of post-operative pain management and 18%of cases are identified as rare case.

Place, publisher, year, edition, pages
2011.
Keyword [en]
rare cases, clustering, case mining, medical
National Category
Computer and Information Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-13164OAI: oai:DiVA.org:mdh-13164DiVA: diva2:450624
Conference
IEEE International Symposium on Signal Processing and Information Technology, 2011
Projects
PainOut
Note
Submitted to: IEEE International Symposium on Signal Processing and Information Technology, 2011Available from: 2011-10-21 Created: 2011-10-21 Last updated: 2017-01-25Bibliographically approved

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Ahmed, Mobyen UddinFunk, Peter
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf