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Mining Rare Cases in Post-Operative Pain by Means of Outlier Detection
Mälardalens högskola, Akademin för innovation, design och teknik. (Intelligent Systems)ORCID-id: 0000-0003-3802-4721
Mälardalens högskola, Akademin för innovation, design och teknik. (Intelligent Systems)ORCID-id: 0000-0002-5562-1424
2011 (engelsk)Manuskript (preprint) (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
2011.
Emneord [en]
rare cases, clustering, case mining, medical
HSV kategori
Forskningsprogram
datavetenskap
Identifikatorer
URN: urn:nbn:se:mdh:diva-13164OAI: oai:DiVA.org:mdh-13164DiVA, id: diva2:450624
Konferanse
IEEE International Symposium on Signal Processing and Information Technology, 2011
Prosjekter
PainOut
Merknad
Submitted to: IEEE International Symposium on Signal Processing and Information Technology, 2011Tilgjengelig fra: 2011-10-21 Laget: 2011-10-21 Sist oppdatert: 2018-01-12bibliografisk kontrollert

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Ahmed, Mobyen UddinFunk, Peter

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