This dissertation research aims to develop systemic methods to analyze mental disorder and social media use data in young adults in a dynamic way. The first part of the dissertation is a comprehensive review on modeling methods of longitudinal data.
The second part describes the methods that we used to identify symptom clusters that can characterize treatment trajectories and to predict responses of anti-depressants for depression patients. Manhattan distance and bottom-up hierarchical clustering methods were used to identify the symptom clusters. Penalized logistic regressions were conducted to identify top baseline predictors of treatment outcomes.
The third part presents of Tweedie distribution application with generalized linear models and growth mixed models for analyzing association between social media use patterns and mental health status. The fourth part is future work and research directions.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/wvqz-w296 |
| Date | January 2024 |
| Creators | Chen, Ying |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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