Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment
arxiv(2024)
摘要
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.
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