Fri. Oct 11, 2019 10:30 AM - 12:10 PM Room 1 (Main Hall)

Organizer / Chair: Roumen MILEV (Department of Psychiatry, Queen's University, Canada), Co-chair: Tadafumi KATO (Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Center for Brain Science, Japan), Discussant: ‌Carlos A ZARATE (NIH / NIMH, USA)

Background: Mood disorders are highly prevalent, associated with significant personal and societal burden. Depression is one of the leading causes of disability world-wide. Bipolar Disorders are associated with high levels of recurrence and pose treatment challenges. Although there are numerous treatment modalities and compounds available, the outcome results are underwhelming. There are no biological tests or markers to predict therapeutic response to a treatment, we don’t know how to predict severity of depression or our next treatment step. We don’t have a good understanding of how to effectively implement evidence-based treatment guidelines, how to change physician prescribing behaviour, or how to use mobile health technology to inform our choices. There is an exponential growth in research endeavours, but their translation to clinical practice, and patient outcomes is severely lacking. This symposium sets a high standard of goals and objectives. Several primers of successful translation of research findings into clinical practice in mood disorders will be presented. Development of evidence-based and clinical practice informed treatment guidelines for management of patients with mood disorders is an example of improving our approach to treatments, but their implementation has not been satisfactory. In this symposium we will present how a point of care app can shift physician prescribing behaviour to become aligned with the guidelines. We will explore the use of mobile health technologies in the clinical decision making and influencing the treatment outcomes. A focus on utilization of machine learning paradigms will exemplify predicting depression severity. Preliminary results of predictors of treatment response in major depressive disorders, as discovered by the large Canadian Biomarkers Integrated Network in depression (CAN-BIND) series of studies will be presented as well. We will have ample opportunities for discussion and commentaries.

Learning Objectives: After attending this symposium the participant will be able:

1. To review CANMAT/ISBD treatment recommendations for management of bipolar disorder
2. To demonstrate the feasibility of using a point of care APP to change physician prescribing behaviour
3. ‌To understand the various approaches to quantify psychiatric disorder severity utilizing information communication technologies.
4. To discuss the difficulty and potential benefit/risk of utilizing machine learning in the psychiatry field.
5. ‌To understand the goals and results of the large CAN-BIND project and the importance of identification of biomarkers for treatment response
6. To understand the concept of digital phenotyping applied to mental health research.
7. ‌To explore the use of mobile health technologies (M-Health) for patient engagement, measurement-based care and monitoring of wellness or relapse in mood disorders