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Traumatic Brain Injury l: Introduction01:28

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Large Language Model-Driven Knowledge Graph Construction in Sepsis Care Using Multicenter Clinical Databases:

Hao Yang1,2,3, Jiaxi Li4, Chi Zhang1

  • 1Department of Critical Care Medicine, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Institutes for Systems Genetics, Sichuan University, West China Hospital, Chengdu, China.

Journal of Medical Internet Research
|March 27, 2025
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) like GPT-4.0 can build comprehensive sepsis knowledge graphs from complex clinical data. This approach enhances sepsis understanding and clinical decision-making, setting a new standard for medical research.

Keywords:
GPT-4.0knowledge graphlarge language modelsprompt engineeringreal-worldsepsis

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Clinical Data Science

Background:

  • Sepsis presents significant heterogeneity and data complexity, challenging traditional knowledge graph construction.
  • Large Language Models (LLMs) offer a novel approach to integrate and analyze unstructured clinical data for improved sepsis management.

Purpose of the Study:

  • To develop a comprehensive sepsis knowledge graph using GPT-4.0 and multicenter clinical databases.
  • To enhance the understanding of sepsis and provide actionable insights for clinical decision-making.
  • To establish a multicenter sepsis database (MSD) to support knowledge graph development.

Main Methods:

  • Collected clinical guidelines, public databases, and real-world data from three hospitals (10,544 sepsis patients).
  • Employed GPT-4.0 with advanced prompt engineering for entity recognition and relationship extraction.
  • Constructed a nuanced sepsis knowledge graph integrating diverse data sources.

Main Results:

  • Established a sepsis database with 10,544 patient records.
  • Developed a sepsis knowledge graph with 1894 nodes and 2021 relationships across nine entity concepts.
  • GPT-4.0 achieved superior F1-scores (76.76% on sepsis data, 65.42% on CMeEE dataset) for entity recognition and relationship extraction compared to other models.

Conclusions:

  • Pioneering use of LLMs (GPT-4.0) for comprehensive sepsis knowledge graph construction.
  • Advanced prompt engineering and multicenter data integration enhanced efficiency and accuracy.
  • The sepsis knowledge graph offers a robust framework for clinical decision-making and future research.