OntoMed-KGTransformer: A Neuro-Symbolic Framework for Clinical Knowledge Fusion

This paper introduces OntoMed-KGTransformer, a novel framework for integrating unstructured clinical text with structured medical knowledge graphs (KGs) and ontologies (specifically, UMLS). Our approach addresses key limitations in clinical decision support by combining: (1) an ontology-guided hierarchical attention mechanism that enforces domain-specific semantic constraints within a Transformer architecture; (2) a dynamic graph-centric tokenization strategy that bridges textual and KG representations; and (3) a dual-encoder architecture with ontology-driven contrastive learning. Evaluations on the MIMIC-III dataset and a custom Hetionet-derived KG demonstrate significant improvements over state-of-the-art baselines (e.g., KG-BERT, BioBERT) in diagnostic prediction, with a 15% relative increase in F1-score. Furthermore, the framework enhances interpretability through the extraction of clinically relevant knowledge paths, promoting trust and transparency in clinical decision-making.