Ontology-Guided Semantic Alignment Of Ehrs And Knowledge Graphs Using Graph Neural Networks For Clinical Representation Learning

Abstract

Semantic heterogeneity and structural fragmentation is present in Electronic Health Records (EHRs) that makes it very hard to achieve the effectiveness of clinical decision support systems and predictive modeling frameworks. This paper has introduced SGCR-AlignNet (Semantic Graph-Coupled Clinical Representation) as a hybrid representation learning model, which combines MIMIC-IV EHR data with Hetionet knowledge graph via ontology-aware semantic embedding alignment. The model is able to learn clinical feature embeddings and graph entity representations simultaneously by minimizing a cross-domain alignment loss and graph structural regularization. This bifurcated optimization can avoid losing either the time series clinical dynamics or deep biomedical medical relational semantics. Through the use of heterogeneity of the relationships between diagnoses, medications, and procedures, SGCR-AlignNet is very promising at capturing latent clinical dependencies that standalone models can often fail to capture. The framework has high fidelity in representation, lowers data sparsity, and better generalization to use in important medical activities, such as mortality prediction and risk stratification. Substantial experimental testing proves that SGCR-AlignNet has consistently better performance in comparison to traditional EHR-only and knowledge graph-only baselines on a variety of performance indices. In addition, semantic graph coupling enhances interpretability by the joining of clinical patterns and biomedical knowledge entities. Altogether, the suggested solution forms a scalable and strong paradigm of knowledge-enhanced clinical AI frameworks, which allow making more accurate, interpretable, and reliable healthcare predictions.