Fuzzy logic for decision support in chronic care.
Computerized clinical guidelines can provide significant benefits in terms of health outcomes and costs, however, their effective computer implementation presents significant problems. Vagueness and ambiguity inherent in natural language (textual) clinical guidelines makes them problematic for formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. In care plan on-line (CPOL), an intranet-based chronic disease care planning system for general practitioners (GPs) in use in South Australia, we formally treat fuzziness in interpretation of quantitative data, formulation of recommendations and unequal importance of clinical indicators. We use expert judgment on cases, as well as direct estimates by experts, to optimize aggregation operators and treat heterogeneous combinations of conjunction and disjunction that are present in the natural language decision rules formulated by specialist teams.
Beliakov
G
G
School of Computing and Mathematics, Deakin University, Rusden campus 662 Blackburn Rd., 3168, Clayton, Australia. gleb@deakin.edu.au
Warren
J
J
D001185:Artificial Intelligence; D002908:Chronic Disease; D003266:Continuity of Patient Care; D003658:Decision Making, Computer-Assisted; D017143:Fuzzy Logic; D019983:Guideline Adherence; D006801:Humans; D007802:Language; D017063:Outcome Assessment, Health Care; D017410:Practice Guidelines as Topic