A Knowledge Graph Framework for Longitudinal Congenital Heart Disease Modeling: Methodology Corner.

BACKGROUND: Congenital heart disease (CHD) is characterized by evolving clinical trajectories; staged interventions; and complex relationships among diagnoses, procedures, and encounters. Conventional machine learning applied to electronic health record (EHR) data often rely on static feature representations that do not preserve the natural longitudinal disease progression or clinical context. OBJECTIVES: The purpose of this study was to develop a knowledge graph-based framework for trajectory-aware representation and analysis of longitudinal CHD EHR data. METHODS: We constructed a longitudinal knowledge graph that integrates patients, encounters, diagnoses, and procedures while preserving temporal ordering across the care continuum. Term frequency-inverse document frequency and Word2Vec approaches were applied to encounter contexts extracted from the graph to generate longitudinal patient representations that supported phenotyping, patient similarity analysis, and retrospective classification. RESULTS: Using accumulated longitudinal EHR from a cardiovascular magnetic resonance cohort, we demonstrate that the framework captured clinically meaningful patterns and supported retrospective identification of single ventricle physiology as a representative use case. CONCLUSIONS: This knowledge graph framework provides a scalable approach for preserving temporal and relational clinical context and enable trajectory-aware modeling of longitudinal cardiovascular EHR data in patients with CHD.