Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment

TL;DR

This paper presents M-KGA, a novel method for automating and enriching medical knowledge graphs by leveraging user input, ontologies, and embeddings to uncover hidden connections and improve completeness.

Contribution

It introduces two new methodologies for discovering hidden links in medical knowledge graphs, enhancing automation and semantic enrichment.

Findings

Effective in enriching medical knowledge graphs with hidden connections

Improves completeness of knowledge graphs using ontology-based semantic enrichment

Demonstrates promising results on EHR-derived medical concepts

Abstract

Knowledge graphs (KGs) serve as powerful tools for organizing and representing structured knowledge. While their utility is widely recognized, challenges persist in their automation and completeness. Despite efforts in automation and the utilization of expert-created ontologies, gaps in connectivity remain prevalent within KGs. In response to these challenges, we propose an innovative approach termed ``Medical Knowledge Graph Automation (M-KGA)". M-KGA leverages user-provided medical concepts and enriches them semantically using BioPortal ontologies, thereby enhancing the completeness of knowledge graphs through the integration of pre-trained embeddings. Our approach introduces two distinct methodologies for uncovering hidden connections within the knowledge graph: a cluster-based approach and a node-based approach. Through rigorous testing involving 100 frequently occurring medical concepts in Electronic Health Records (EHRs), our M-KGA framework demonstrates promising results, indicating its potential to address the limitations of existing knowledge graph automation techniques.