Prediction of the evolution of bipolar depression using Semantic Web TechnologiesPrediction of the evolution of bipolar depression using Semantic Web Technologies Περίληψη Δημοσίευσης σε Συνέδριο Conference Paper Abstract 2015-10-282014enIn 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. http://creativecommons.org/licenses/by/4.0/ 5th International Conference on Information, Intelligence, Systems and Applications Petrakis Evripidis Πετρακης Ευριπιδης Bei Aikaterini Μπεη Αικατερινη Thermolia Chryso Θερμολια Χρυσω IEEE