Fuzzy modelling of the expert's knowledge in ECG-based ischaemia detection
Fuzzy Sets and Systems - Special issue on fuzzy signal processing
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Fuzzy set theory—and its applications (3rd ed.)
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Automatic Ontology-Based Knowledge Extraction from Web Documents
IEEE Intelligent Systems
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IEEE Intelligent Systems
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Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
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IEEE Intelligent Systems
A genetic fuzzy agent using ontology model for meeting scheduling system
Information Sciences: an International Journal
A fuzzy ontology and its application to news summarization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ontology development for unified traditional Chinese medical language system
Artificial Intelligence in Medicine
Ontological fuzzy agent for electrocardiogram application
Expert Systems with Applications: An International Journal
Ontology-based fuzzy support agent for ship steering control
Expert Systems with Applications: An International Journal
SHOMAS: Intelligent guidance and suggestions in shopping centres
Applied Soft Computing
HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis
Applied Intelligence
An ontology-based hierarchical semantic modeling approach to clinical pathway workflows
Computers in Biology and Medicine
Intelligent ontological multi-agent for healthy diet planning
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation
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An approximation to the computational theory of perceptions using ontologies
Expert Systems with Applications: An International Journal
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In recent years, the population has been aging gradually, and the number of patients with chronic respiratory disease has grown increasingly; therefore the respiratory healthcare plays an important role in the clinical care. This paper presents an ontology-based intelligent healthcare agent for the respiratory waveform recognition to assist the medical staff in judging the meaning of the graph reading from ventilators. The intelligent healthcare agent contains three modules, including the respiratory waveform ontology, ontology construction mechanism, and fuzzy recognition agent, to classify the respiratory waveform. The respiratory waveform ontology represents the respiratory domain knowledge, which will be utilized to classify and recognize the respiratory waveform by the intelligent healthcare agent. The ontology construction mechanism will infer the fuzzy numbers of each respiratory waveform from the patient or respiratory waveform repository. Next, the fuzzy recognition agent will classify and recognize the respiratory waveform into different types of respiratory waveforms. Finally, after the confirmation of medical experts, the classified and recognized results are stored in the classified waveform repository. The experimental results show that our approach can classify and recognize the respiratory waveform effectively.