Change search
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
Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-9607-9504
Örebro University, School of Science and Technology. (AASS)
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-3122-693X
2015 (English)In: Proceedings of the 8th International conference of Health Informatics (HEALTHINF 2015), SciTePress, 2015Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an approach to automatically mine rules in time series data representing physiologicalparameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined foreach physiological time series data. The generated rules based on the prototypical patterns are then describedin a textual representation which captures trends in each physiological parameter and their relation to the otherphysiological data. In this paper, a method for measuring similarity of rule sets is introduced in order tovalidate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classesin the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rulemining technique is able to acquire a distinctive model for each clinical condition, and represent the generatedrules in a human understandable textual representation

Place, publisher, year, edition, pages
SciTePress, 2015.
Keyword [en]
rule mining, pattern abstraction, health parameters, physiological time series, clinical condition.
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-39650ISBN: 978-989-758-068-0 (print)OAI: oai:DiVA.org:oru-39650DiVA: diva2:771473
Conference
HEALTHINF 2015 : HEALTHINF 8th International Conference on Health Informatics, 12-15 january, Lisabon, Portugal
Available from: 2014-12-14 Created: 2014-12-14 Last updated: 2017-10-18Bibliographically approved

Open Access in DiVA

fulltext(370 kB)140 downloads
File information
File name FULLTEXT01.pdfFile size 370 kBChecksum SHA-512
77911d1a69509e97528a3a95c210e7a1affb061bee4e41e144202ad5bff309e481a571f4778175bbabbd92a9fb552973c4243fba4e23d107c0ef415d6b4ac76e
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Banaee, HadiAhmed, Mobyen UddinLoutfi, Amy
By organisation
School of Science and Technology
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 140 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 508 hits
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