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Mining Rare Cases in Post-Operative Pain by Means of Outlier Detection
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0002-5562-1424
2011 (English)In: IEEE Symposium on Signal Processing and Information Technology (ISSPIT) 2011, IEEE , 2011, p. 35-41Conference paper, Published paper (Refereed)
Abstract [en]

Rare cases are often interesting for health professionals, physicians, researchers and clinicians in order to reuse and disseminate experiences in healthcare. However, mining, i.e. identification of rare cases in electronic patient records, is non-trivial for information technology. This paper investigates a number of well-known clustering algorithms and finally applies a 2 nd order clustering approach by combining the Fuzzy C-means algorithm with the Hierarchical one. The approach was used to identify rare cases from 1572 patient cases in the domain of post-operative pain treatment. The results show that the approach enables the identification of rare cases in the domain of post-operative pain treatment and 18% of cases were identified as rare

Place, publisher, year, edition, pages
IEEE , 2011. p. 35-41
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-13605DOI: 10.1109/ISSPIT.2011.6151532ISI: 000300709900007Scopus ID: 2-s2.0-84857871506ISBN: 978-1-4673-0753-6 (print)OAI: oai:DiVA.org:mdh-13605DiVA, id: diva2:466148
Conference
11th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2011;Bilbao;14 December 2011 through 17 December 2011
Available from: 2011-12-15 Created: 2011-12-15 Last updated: 2017-01-25Bibliographically approved

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Ahmed, Mobyen UddinFunk, Peter

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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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