Leveraging Social Determinants of Health (SDoH) Knowledge Graph to Identify Latent Patterns in Veteran Suicide Risk

While Social Determinants of Health (SDoH) are widely acknowledged as critical factors influencing health outcomes, particularly in vulnerable populations, their complex relationships and systemic impacts remain insufficiently examined. This study presents the development and systematic analysis of a comprehensive knowledge graph (KG) framework designed to elucidate the complex relationships between SDoH and mental health outcomes in a high-risk population: veterans with documented histories of suicide attempts or suicidal ideation. Leveraging a comprehensive electronic health records dataset from the U.S. Veterans Health Administration, we generated synthetic data that accurately preserves the statistical properties of the original dataset. We also constructed a specialized SDoH knowledge graph to enable multidimensional analysis. Using topological link prediction and node classification algorithms, we systematically analyzed structural patterns across critical SDoH domains to uncover latent relationships within the KG. Our KG-based approach enables privacy-preserving health disparities research by combining synthetic data generation with graph-based analytics. Our results demonstrate the viability of this approach for deriving clinically meaningful insights while maintaining strict confidentiality protections, establishing a scalable paradigm for future population health studies.