Facilitating Online Healthcare Support Group Formation Using Topic Modeling

Abstract

Patients increasingly seek peer support in online health forums; however, the large-scale and inconsistent engagement patterns of these forums often fail to meet patients’ support needs effectively. Smaller, personalized support groups could address these challenges by tailoring interactions to users’ shared experiences and demographics. This study introduces the Group-specific Dirichlet Multinomial Regression (gDMR) model, a structured framework for automating and personalizing support group formation using user-generated content, demographic, and interaction data. By incorporating group-specific parameters and node embeddings, gDMR captures nuanced demographic and behavioral patterns, extending traditional Dirichlet Multinomial Regression (DMR). Experiments demonstrate gDMR’s ability to form groups that are more semantically coherent and contextually relevant than those produced by baseline models. This scalable model reduces manual effort in group personalization, fostering inclusive engagement and enhancing patient-centered care. Findings highlight gDMR’s potential as a framework for digital healthcare platforms, from online health communities to healthcare systems utilizing electronic health records (EHRs), advancing health informatics through support group formation.