title: Decision support system for classification of epilepsies in childhood.
abstract: Diagnosis of epilepsy in childhood is often difficult as the symptoms are often atypical and the epilepsy syndromes are multiform. Methods from the domain of artificial intelligence give the opportunity to formalize medical knowledge and standardize various diagnostic procedures in specific domains of medicine. We developed a decision support system using artificial intelligence techniques for the classification and ultimately the diagnosis of epilepsies and epilepsy syndromes in children. The system incorporates knowledge from the International Classification of Epilepsies and Epileptic Syndromes. It was assessed using clinical data and the system's conclusions were compared with the diagnoses proposed by an experienced doctor. The system and the physician reached identical diagnoses in 85.2% of the cases. In an additional 8.2% of the cases, the system's diagnosis was similar to that of the physician, thus raising its overall success rate to 93.4%. The system can be helpful, especially for trainees, since it only needs to import the clinical and laboratory data. Decision making and differential diagnosis are then performed automatically.
authors.affiliation: University of Crete, School of Medicine, Heraklion, Greece. kostas@cs.teiher.gr
authors.forename: Kostas M
authors.initials: KM
authors.lastname: Vassilakis
authors.forename: Lagia
authors.initials: L
authors.lastname: Vorgia
authors.forename: Sifis
authors.initials: S
authors.lastname: Micheloyannis
mesh_terms: D002648:Child; D020000:Decision Support Systems, Clinical / Q000295:instrumentation*; D003937:Diagnosis, Differential; D004569:Electroencephalography; D004827:Epilepsy / Q000145:classification* / Q000175:diagnosis*; D006801:Humans