Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Varieties of knowledge elicitation techniques
International Journal of Human-Computer Studies
Formal ontology, conceptual analysis and knowledge representation
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Understanding, building and using ontologies
International Journal of Human-Computer Studies
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Comments on "Distinguishability quantification of fuzzy sets"
Information Sciences: an International Journal
Managing uncertainty and vagueness in description logics for the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
Reasoning with very expressive fuzzy description logics
Journal of Artificial Intelligence Research
Building a case-based diet recommendation system without a knowledge engineer
Artificial Intelligence in Medicine
A fuzzy operator for the enhancement of blurred and noisy images
IEEE Transactions on Image Processing
Computer Methods and Programs in Biomedicine
Ontology driven decision support for the diagnosis of mild cognitive impairment
Computer Methods and Programs in Biomedicine
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The use of Magnetic Resonance (MR) as a supporting tool in the diagnosis and monitoring of multiple sclerosis (MS) and in the assessment of treatment effects requires the accurate determination of cerebral white matter lesion (WML) volumes. In order to automatically support neuroradiologists in the classification of WMLs, an ontology-based fuzzy decision support system (DSS) has been devised and implemented. The DSS encodes high-level, specialized medical knowledge in terms of ontologies and fuzzy rules and applies this knowledge in conjunction with a fuzzy inference engine to classify WMLs and to obtain a measure of their volumes. The performance of the DSS has been quantitatively evaluated on 120 patients affected by MS. Specifically, binary classification results have been first obtained by applying thresholds on fuzzy outputs and then evaluated, by means of ROC curves, in terms of trade-off between sensitivity and specificity. Similarity measures of WMLs have been also computed for a further quantitative analysis. Moreover, a statistical analysis has been carried out for appraising the DSS influence on the diagnostic tasks of physicians. The evaluation has shown that the DSS offers an innovative and valuable way to perform automated WML classification in real clinical settings.