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A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome
یک سیستم هوش مصنوعی پیشنهادی برای حمایت از تشخیص صرع بر اساس طبقه بندی صرع 2017: نشان داده شده توسط سندرم دراوت-2020 Purpose: The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiologybased
approach.We developed a propositional artificial intelligence (AI) system based on the above concepts to
support physicians in the diagnosis of epilepsy.
Methods:We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy
syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be
close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsyrelated
knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic
electroencephalography (EEG) findings, treatments, etc. We used the OntologyWeb Language with Description
Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship
between these ontologies.
Results: Dravet syndrome was taken as an illustration for epilepsy syndromes implementation.We designed an
interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old
boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drools execution, we
successfully demonstrate the process of differential diagnosis.
Conclusion: We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the
complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy
syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis. Keywords: Epilepsy syndrome | Etiology | OWL-DL | Protégé | Seizure classification | SemanticWeb Rule Language |
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