Problems in establishing the medical expert systems CADIAG-1 and CADIAG-2 in rheumatology
Journal of Medical Systems
Fuzzy set theory in medical diagnosis
IEEE Transactions on Systems, Man and Cybernetics
Representation and semiautomatic acquisition of medical knowledge in CADIAG-1 and CADIAG-2
Computers and Biomedical Research
Mesicar-a medical expert system integrating causal and associative reasoning
Applied Artificial Intelligence
Validation of the medical expert system RENOIR
Computers and Biomedical Research
Artificial Intelligence in Medicine
Consistency checking of binary categorical relationships in a medical knowledge base
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Editorial: fuzzy set and possibility theory-based methods in artificial intelligence
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
On the (fuzzy) logical content of CADIAG-2
Fuzzy Sets and Systems
The consistency of the CADIAG-2 knowledge base: a probabilistic approach
LPAR'10 Proceedings of the 17th international conference on Logic for programming, artificial intelligence, and reasoning
Measuring and repairing inconsistency in probabilistic knowledge bases
International Journal of Approximate Reasoning
Measuring and repairing inconsistency in knowledge bases with graded truth
Fuzzy Sets and Systems
Plausible reasoning and graded information: A unified approach
Fuzzy Sets and Systems
Formal approaches to rule-based systems in medicine: The case of CADIAG-2
International Journal of Approximate Reasoning
The Consistency of the Medical Expert System CADIAG-2: A Probabilistic Approach
Journal of Information Technology Research
Exploiting 3d part-based analysis, description and indexing to support medical applications
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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As part of a plan to promote semi-automatic knowledge acquisition for the medical consultant system CADIAG-II/RHEUMA, this study sought to explore and cope with the variability of results that may be anticipated when performing knowledge acquisition with patient data from different patient settings. Patient data were drawn both from a published study for the classification of rheumatoid arthritis (RA) and from a large database of rheumatological patient charts developed for the CADIAG-II/RHEUMA system. An analysis of the relationships between RA and selected CADIAG-II/RHEUMA symptoms was done using two models. In one of them, we controlled for the differences in baseline frequencies of symptoms and diseases in the two study populations as an important factor influencing the results of the calculations. Other factors that were identified included inconsistent definitions of symptoms and diseases, and the different composition of study groups in the two study populations. By eliminating differences in baseline frequencies as the most important bias, the results obtained from the two different knowledge sources became more consistent. All remaining inconsistencies and uncertainties about the contribution and relative importance of the factors were formalized using fuzzy intervals.