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Related Experiment Video

Updated: May 24, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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Published on: October 13, 2023

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

Hyeyoon Moon1, Eunhye Jang1, Won Chul Cha1,2

  • 1Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

We created an AI pipeline to build knowledge graphs from unstructured emergency department (ED) problem lists. Llama 3.1 70B demonstrated superior performance, enhancing data organization for better clinical insights.

Keywords:
Information ExtractionKnowledge GraphProblem Lists

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Knowledge Representation

Background:

  • Emergency department (ED) problem lists are crucial for patient care but remain unstructured.
  • Lack of structure hinders efficient data retrieval and analysis in clinical settings.

Purpose of the Study:

  • To develop an information extraction pipeline for constructing knowledge graphs from ED problem lists.
  • To compare the effectiveness of different normalization strategies and large language models (LLMs) for this task.

Main Methods:

  • An information extraction pipeline was developed.
  • Three normalization strategies and five LLMs were evaluated on 250 annotated ED problem lists.
  • Performance was assessed using Exact F1 and Partial F1 scores.

Main Results:

  • Llama 3.1 70B with Named Entity Disambiguation achieved optimal performance, with Exact F1 of 0.65 and Partial F1 of 0.70.
  • This approach improved F1 scores by 6.56% compared to using raw text.
  • A knowledge graph was constructed from 2,000 problem lists, containing 1,983 entities and 3,208 relations.

Conclusions:

  • The developed pipeline offers a practical framework for large-scale ED knowledge graph construction.
  • LLMs, particularly Llama 3.1 70B, show significant potential in structuring clinical data.
  • This work paves the way for improved data utilization in emergency medicine.