title: Intelligent clinical decision support systems based on SNOMED CT.
abstract: The decision support systems that have been developed to assist physicians in the diagnostic process often are based on static data which may be out of date. We present a comprehensive analysis of artificial intelligent methods which could be applied to documents encoded by SNOMED CT. By mining information directly from SNOMED CT encoded documents, a decision support system could contain timely updated diagnostic information, which is of significant value in fast changing situations such as minimally understood emerging diseases and epidemics. Through a high level comparison of many AI methods it is found that a TAN-Bayesian method could be the most suitable to apply to SNOMED CT data.
authors.affiliation: Faculty of Health Science, University of Ontario Institute of Technology 2000 Simcoe Street North, Oshawa, Canada. ewelina.ciolko@uoit.ca
authors.forename: Ewelina
authors.initials: E
authors.lastname: Ciolko
authors.forename: Fletcher
authors.initials: F
authors.lastname: Lu
authors.forename: Amardeep
authors.initials: A
authors.lastname: Joshi
mesh_terms: D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D039061:Systematized Nomenclature of Medicine