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Automated Information Extraction Pipeline for Constructing ED Knowledge Graphs from Korean Clinical Problem Lists

Year: 2026

Type: book-chapter

Abstract: Emergency department problem lists remain unstructured despite documenting major medical problems. We developed an information extraction pipeline for constructing ED knowledge graphs, comparing three normalization strategies and five LLMs on 250 annotated Samsung Medical Center ED problem lists. Llama 3.1 70B with Named Entity Disambiguation achieved optimal performance (Exact F1: 0.65, Partial F1: 0.70), improving F1 by 6.56% over raw text. The resulting knowledge graph from 2,000 problem lists contains 1,983 entities and 3,208 relations, establishing a practical framework for large-scale ED knowledge graph construction.

Source: Studies in health technology and informatics

DOI: 10.3233/shti260360

Authors: Hyeyoon Moon, Eunhye Jang, Won Chul Cha

Institutions: Sungkyunkwan University, Samsung Medical Center

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7161998278

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