MARCH 29, 2024
George D. Price, PhD
AI Scientist
Artisight, Inc.
About the Presentation: Major depressive disorder (MDD) is a debilitating and heterogenous mental health disorder that is characterized by symptoms including low mood, issues with sleep, psychomotor difficulties, and fatigue. MDD affects one in twenty adults worldwide and has shown increased prevalence in the United States over the past twenty years. As such, efforts to effectively screen, diagnose, and treat MDD are paramount. However, to address these concerns, an increased understanding of an individual’s daily behavior is required, which is not adequately captured by infrequent clinical visits. One such method for consistent, longitudinal observation, passively-collected accelerometry, serves as an observational method for unobtrusively capturing movement, sedentary and sleep behaviors in real-time. Therefore, the primary effort of this work is to investigate the utility of leveraging longitudinal, passively-collected accelerometer information in detecting and predicting outcomes related to MDD. A combination of unsupervised machine learning, supervised machine learning, and deep learning techniques were used to characterize movement, sedentary and sleep behaviors for individuals with MDD, as well as investigate depression presence, individual depressive symptoms, long-term depression variability, and acute depression variability.
About the Presenter: George is a recent graduate of the Quantitative Biomedical Sciences doctoral program at Dartmouth College. George received his undergraduate degree in Biology at Boston College, while working as a research intern with the Brain Trauma lab in the Pediatric Neurosurgery department at Massachusetts General Hospital. Following his undergraduate degree, George worked full-time with the Brain Trauma lab for two years before joining QBS. Currently, George is interested in leveraging passively-collected data with traditional machine learning and deep learning approaches to better understand mental health disorder detection and symptomatology.