Medical knowledge extraction. Applications of data analysis methods
1992 (English)Licentiate thesis, comprehensive summary (Other academic)
In this thesis we explore and discuss some important methods for knowledge extraction from meclical data. This is done in relation to, and for the purpose of design and development of decision support systems, which could be population specific.
To test data and extract knowledge, we use univariate and multivariate statistical methods, the rough sets theory and probabilistic artificial intelligence approaches. These methods are used to estimate characteristics of patient groups, disease profiles and other features relevant for medical problems. In particular, we apply them to clifferentiate among patient groups, develop patient models and derive decision rules. Our experience refers to two medical domains (patients with diagnosed and non-diagnosed, but suspected liver disease and patients with duodenal ulcer surgery).
Extracted knowledge can be used both in clinical practice and health care programs, as well as in computer based decision support systems to adjust them to various clinical environments.
Place, publisher, year, edition, pages
Linköping: Linköpings universitet , 1992. , 22 + 4 papers p.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 311
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:liu:diva-29511Local ID: LiU-Tek-Lic 1992:03ISBN: 91-7870-871-0OAI: oai:DiVA.org:liu-29511DiVA: diva2:250326
1992-02-14, Föreläsningssalen, IMT, plan 11, Lab II-byggnaden, Campus US, Linköpings universitet, Linköping, 14:00 (English)