Transfer Topic Modeling for Identifying Depression Subtypes in Youth

Depression is increasing among adolescents and young adults. Identifying clinically meaningful subtypes of depression and comorbidity patterns in youth is therefore a critical priority. Topic modeling of electronic health records (EHR) offers a promising strategy to uncover latent depression phenotypes and patient subtypes underlying diagnostic co-occurrence. Yet two analytical barriers remain: existing methods often lack the efficiency needed for large EHR datasets with high-dimensional features, and the smaller, sparser records common in youth populations hinder estimation stability. To overcome these challenges, we propose a transfer topic modeling approach that integrates the computationally efficient Topic-SCORE algorithm with a ridge-type estimator to stabilize latent subspace estimation and improve topic matrix recovery. Simulation studies show our method outperforms models trained only on the target population and alternative transfer learning approaches. Leveraging the All of Us Research Program, our method identifies seven clinically meaningful latent structures of youth depression and distinguishes subgroups at elevated risk for suicidal thoughts and behaviors.

Electronic health records

Representation learning

Transfer learning

Mental health

Suicide risk 

International Society for Bayesian Analysis (ISBA)