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A synthesis of fuzzy rule-based system verification
Fuzzy Sets and Systems
A glance at implication and T-conditional functions
Discovering the world with fuzzy logic
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Fuzzy Control and Fuzzy Systems
Fuzzy Rule Base Systems Verification Using High-Level Petri Nets
IEEE Transactions on Knowledge and Data Engineering
Fuzzy Logic in Clinical Practice Decision Support Systems
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 5 - Volume 5
Interpretability constraints for fuzzy information granulation
Information Sciences: an International Journal
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Data driven generation of fuzzy systems: an application to breast cancer detection
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On generating FC3 fuzzy rule systems from data usingevolution strategies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Checking the coherence and redundancy of fuzzy knowledge bases
IEEE Transactions on Fuzzy Systems
Knowledge-based verification of clinical guidelines by detection of anomalies
Artificial Intelligence in Medicine
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Clinical practice guidelines are expected to promote more consistent, effective, and efficient medical practices and improve health outcomes, especially if provided in the form of clinical decision support. However, most clinical guidelines, especially when expressed in the form of condition-action recommendations, embody different kinds of structural errors that compromise their practical value. With this respect, this paper presents a novel method for verifying the reliability of condition-action clinical recommendations encoded in the form of fuzzy rules, with the final aim of determining inconsistency, redundancy and incompleteness anomalies in a very simple and understandable fashion. The method is based on general definitions of inconsistency, redundancy and incompleteness for fuzzy clinical rules in terms of similarity between antecedents and consequents, bringing them near the imprecise character of fuzzy decision support systems. A key issue relies on the formalization of fuzzy degrees for these anomalies that can be simply interpreted by the final users as measurements suggesting the modifications to be performed to the clinical rules in order to eliminate or mitigate the existing undesired effects. The method has been profitably assessed on two sample sets of clinical rules: the first one identified from the relevant clinical literature and the second one extracted automatically by machine learning techniques from a widely known clinical database. The achieved results prove simplicity and usability of our method in detecting structural anomalies and in adjusting a rule base by exploiting information carried out during the verification phase.