A Resolution-Alignment-Completeness System for Diagnosis Code Imputation in Clinical Knowledge Graphs

The rapid growth of electronic health records (EHR) presents challenges in data integration and interoperability due to the incomplete nature of this information, limiting its effective utilization. While ontology-based data integration across diverse resources has been widely practiced, the process of codifying records remains error-prone and largely manual. Knowledge Graph Embeddings as an alternative solution can provide for efficient quality data representations. In this paper, we propose an embedding-based system that applies entity resolution and alignment across medical terminologies and ontologies for imputing codified data. Through experimentation we demonstrate the benefits of the proposed solution for semantic completion and consistency tasks in terms of NDCG@K and Sem@K.