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Prediction of the evolution of bipolar depression using Semantic Web Technologies

Petrakis Evripidis, Bei Aikaterini, Thermolia Chryso

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URIhttp://purl.tuc.gr/dl/dias/1FEBD59D-A108-4D95-9413-07032D72E527-
Identifierhttp://www.intelligence.tuc.gr/~petrakis/publications/BIPOLAR-IISA2014.pdf-
Languageen-
TitlePrediction of the evolution of bipolar depression using Semantic Web Technologiesen
CreatorPetrakis Evripidisen
CreatorΠετρακης Ευριπιδηςel
CreatorBei Aikaterinien
CreatorΜπεη Αικατερινηel
CreatorThermolia Chrysoen
CreatorΘερμολια Χρυσωel
PublisherIEEEen
Content SummaryIn our study we present a design for a decision support system for patients suffering from Bipolar Disorder (BD). Bipolar Disorder is a recurrent and highly disabling psychiatric illness that evolves constantly in time and often leads to crucial incidents. We focus on Bipolar Depression and especially on a Breakthrough Depressive Episode scenario that occurs when a patient shows depressive symptoms during pharmaceutical treatment. Using Semantic Web Technologies we developed SybillaTUC, a prototype Clinical Decision Support System which combines the clinical guidelines for Bipolar Disorder with a patient's condition and his medical record. The system is able to predict the evolution of the disease for each patient, alerting the clinician on the possibility of a crucial incident suggesting optimal treatment. en
Type of ItemΠερίληψη Δημοσίευσης σε Συνέδριοel
Type of ItemConference Paper Abstracten
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-28-
Date of Publication2014-
Bibliographic CitationChrysa H. Thermolia, Ekaterini S. Bei, and Euripides G.M. Petrakis. Prediction of the Evolution of Bipolar Depression using Semantic Web Technologies.(2014, Jul.) Presented at 5th International Conference on Information, Intelligence, Systems and Applications (IISA 2014).[Online]. Available:http://www.intelligence.tuc.gr/~petrakis/publications/BIPOLAR-IISA2014.pdfen

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