Knowledge-guided graph co-training for differential diagnosis of rare diseases

Thomas M. Menino Convention & Exhibition Center 

Rare diseases are frequently associated with prolonged diagnostic delays and complex differential diagnosis pathways, driven by heterogeneous clinical presentations and overlapping symptomatology with more common phenotypes. We propose a knowledge-guided graph co-training framework that integrates longitudinal electronic health record (EHR) data with external biomedical knowledge to improve rare disease detection and characterize diagnostic trajectories. Our approach jointly trains across multiple graph modalities, including patient-derived EHR co-occurrence graphs, general biomedical knowledge graphs, and disease-specific knowledge graphs. Through co-training, the model leverages complementary information across graph sources and related phenotypes to learn clinically informed patient representations from both codified and narrative data. By incorporating relationships among diagnoses, medications, procedures, and symptoms, the framework captures evolving patterns of disease presentation and competing differential diagnoses that often precede definitive diagnosis. We demonstrate that multi-graph co-training can identify clinically meaningful diagnostic patterns associated with delayed recognition and misclassification across rare disease populations. These findings highlight the potential of knowledge-guided graph learning to support earlier rare disease detection and improve understanding of diagnostic complexity in high-dimensional EHR data.

electronic health records

rare diseases

graph neural networks

representation learning

diagnostic delay

differential diagnosis