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
Automatic Early Risk Detection of Possible Medical Conditions for Usage Within an AMI-System
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab, SAIL)ORCID iD: 0000-0003-2949-4123
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Distributed Real-Time Systems, DRTS)ORCID iD: 0000-0002-5223-4381
2015 (English)In: Ambient Intelligence - Software and Applications / [ed] Amr Mohamed, Paulo Novais, António Pereira, Gabriel Villarrubia González, Antonio Fernández-Caballero, Springer Berlin/Heidelberg, 2015, 13-21 p.Conference paper, Published paper (Refereed)
Abstract [en]

Using hyperglycemia as an example, we present how Bayesian networks can be utilized for automatic early detection of a person’s possible medical risks based on information provided by un obtrusive sensors in their living environments. The network’s outcome can be used as a basis on which an automated AMI-system decides whether to interact with the person, their caregiver, or any other appropriate party. The networks’ design is established through expert elicitation and validated using a half-automated validation process that allows the medical expert to specify validation rules. To interpret the networks’ results we use an output dictionary which is automatically generated for each individual network and translates the output probability into the different risk classes (e.g.,no risk, risk).

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2015. 13-21 p.
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 376
Keyword [en]
Ambient Assisted Living, Bayesian networks, Automated Diagnosis
National Category
Computer Science
Identifiers
URN: urn:nbn:se:his:diva-11171DOI: 10.1007/978-3-319-19695-4_2Scopus ID: 2-s2.0-84937501569ISBN: 978-331919694-7 (print)OAI: oai:DiVA.org:his-11171DiVA: diva2:844418
Conference
6th International Symposium on Ambient Intelligence (ISAmI 2015)
Projects
Helicopter
Available from: 2015-08-06 Created: 2015-06-18 Last updated: 2017-11-27Bibliographically approved

Open Access in DiVA

fulltext(355 kB)279 downloads
File information
File name FULLTEXT02.pdfFile size 355 kBChecksum SHA-512
adec8827e4a076f83d1b414923c3a039e2c8a5a6e39b305a5718cee6ba811575bb3abe0e8c32364fe128d041c967feb25d546f6b1264a550fd443d15e961d62c
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Steinhauer, H. JoeMellin, Jonas
By organisation
School of InformaticsThe Informatics Research Centre
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 279 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

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 637 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