Prediction of the evolution of bipolar depression using semantic web technologies

Abstract

In 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.