What are fuzzy rules and how to use them
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Centroid of a type-2 fuzzy set
Information Sciences: an International Journal
Fuzzy Logic in Clinical Practice Decision Support Systems
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 5 - Volume 5
Computing with words and its relationships with fuzzistics
Information Sciences: an International Journal
Possibility theory and statistical reasoning
Computational Statistics & Data Analysis
A constructive method for the definition of interval-valued fuzzy implication operators
Fuzzy Sets and Systems
An Interval Type-2 Fuzzy multiple echelon supply chain model
Knowledge-Based Systems
A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation
IEEE Transactions on Fuzzy Systems
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
Multi-attribute group decision making models under interval type-2 fuzzy environment
Knowledge-Based Systems
Type-2 fuzzy sets and systems: an overview
IEEE Computational Intelligence Magazine
IEEE Transactions on Fuzzy Systems
Computing derivatives in interval type-2 fuzzy logic systems
IEEE Transactions on Fuzzy Systems
A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots
IEEE Transactions on Fuzzy Systems
Encoding Words Into Interval Type-2 Fuzzy Sets Using an Interval Approach
IEEE Transactions on Fuzzy Systems
Linguistic Summarization Using IF–THEN Rules and Interval Type-2 Fuzzy Sets
IEEE Transactions on Fuzzy Systems
Fuzzy logic for decision support in chronic care
Artificial Intelligence in Medicine
Evaluating cardiac health through semantic soft computing techniques
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Fuzzy Ontologies and Fuzzy Markup Language Applications
Fuzzy expert system approach for coronary artery disease screening using clinical parameters
Knowledge-Based Systems
SITIS '12 Proceedings of the 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems
Effects of type reduction algorithms on forecasting accuracy of IT2FLS models
Applied Soft Computing
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In the last years, the advent of Decision Support Systems (DSSs) embedding Clinical Practice Guidelines (CPGs) has created the premise for improving quality of care and patient safety. However, CPGs, typically encoded in the form of if-then rules, are still not completely suitable for computer implementation, due to different kinds of uncertainty affecting them. In order to face this issue, this paper proposes a novel approach for automatically encoding CPGs by means of if-then rules based on interval type-2 fuzzy sets, with the final aim of dealing with two different kinds of uncertainty, namely intra-guideline uncertainty and inter-guideline uncertainty. The approach is structured into four sequential steps: (i) the encoding of multiple and different CPGs concerning a same problem as if-then rules built on the top of crisp sets; (ii) the mapping of these crisp sets first into possibility distributions and, then, into type-1 fuzzy sets; (iii) the construction of final interval type 2 fuzzy sets; and (iv) the specification of fuzzy rules on the top of the interval type 2 fuzzy sets produced. As a proof of concept, the approach is employed to deal with some CPGs pertaining the hypertension treatment, showing its feasibility and also suggesting that its application could simply and proficiently aid the embedding of CPGs into clinical DSSs.