Biostatistics Seminar: "Machine Learning Approaches for Optimizing Treatment Strategies for Mental Disorders"

NOTE: BIS 526 students are required to attend in person (47 College St., Room 106A). All others are requested to attend via Zoom.

Speaker- Yuanjia Wang, Ph.D.

Title- "Machine Learning Approaches for Optimizing Treatment Strategies for Mental Disorders"

Abstract

Among currently available pharmacological and behavioral interventions for mental disorders, no single therapy is universally effective. Moreover, treatment responses are far from adequate across mental disorders. As such, there is an urgent need to optimize treatment responses. Various factors appear to be associated with positive treatment responses, thus providing evidence for improving response rate by incorporating patient-specific characteristics in treatment decisions in an effort to achieve precision psychiatry. However, individualized treatment decision making for mental disorders faces challenges of extensive diagnostic heterogeneity, substantial between-patient variation in biological and clinical disease manifestation, and mismatch between diagnostic categorization and the underlying pathophysiology. We propose novel machine learning methods to address emerging challenges through probabilistic generative models and neural networks. We discuss several studies to discover reliable individualized treatment strategies that factor in a patient’s clinical, psychosocial, and biological markers, and integrate evidence from multi-domain data sources and multiple studies to increase generalizability and reproducibility.