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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 (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.
, Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 376
Keyword [en]
Ambient Assisted Living, Bayesian networks, Automated Diagnosis
National Category
Computer Science
URN: urn:nbn:se:his:diva-11171DOI: 10.1007/978-3-319-19695-4_2ScopusID: 2-s2.0-84937501569ISBN: 978-331919694-7OAI: diva2:844418
6th International Symposium on Ambient Intelligence (ISAmI 2015)
Available from: 2015-08-06 Created: 2015-06-18 Last updated: 2016-03-03Bibliographically approved

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