Fuzzy set theory in medical diagnosis
IEEE Transactions on Systems, Man and Cybernetics
Performance evaluation of medical expert systems using ROC curves
Computers and Biomedical Research
Clinical monitoring with fuzzy automata
Fuzzy Sets and Systems
Journal of Biomedical Informatics
Evaluation of Fuzzy Relation Method for Medical Decision Support
Journal of Medical Systems
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The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences pursues methodological research in and practical development of knowledge-based computer systems to assist in the decision-making processes for all areas of medical application. Vagueness of medical terms, uncertainty in the co-occurrence of medical entities, and incompleteness in medical theories are well-known characteristics of medical knowledge and ought to be considered in practically-used medical knowledge-based systems. We found that fuzzy set theory and fuzzy logic are powerful theories that model the above-mentioned characteristics. Fuzzy set theory and fuzzy logic were applied in the following systems: CADIAG-II and MedFrame/CADIAG-IV, FuzzyARDS, and FuzzyKBWean. CADIAG-II and MedFrame/CADIAG-IV are framework programs for consultation systems to aid in the differential diagnostic process in internal medicine. FuzzyARDS is an intelligent on-line monitoring program of data from patients with acute respiratory distress syndrome (ARDS) at an intensive care unit (ICU). It employs fuzzy trend detection and fuzzy automata. FuzzyKBWean is an open-loop fuzzy control program for optimization and quality control of the ventilation and weaning process of patients after cardiac surgery at the ICU. The above-mentioned computer systems have reached the state of extensive clinical integration and testing at the Vienna General Hospital. The obtained results show the applicability and usefulness of these systems.