Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models
Marc Steiner
Artificial intelligence (AI) is reshaping modern healthcare by advancing disease diagnosis, treatment decision-making, and biomedical research. Among AI technol...
Reporting by Zhengda Wang, SwissFinanceAI Redaktion
Abstract
Artificial intelligence (AI) is reshaping modern healthcare by advancing disease diagnosis, treatment decision-making, and biomedical research. Among AI technologies, large language models (LLMs) have become especially impactful, enabling deep knowledge extraction and semantic reasoning from complex medical texts. However, effective clinical decision support requires knowledge in structured, interoperable formats. Knowledge graphs serve this role by integrating heterogeneous medical information into semantically consistent networks. Yet, current clinical knowledge graphs still depend heavily on manual curation and rule-based extraction, which is limited by the complexity and contextual ambiguity of medical guidelines and literature. To overcome these challenges, we propose an automated framework that combines retrieval-augmented generation (RAG) with LLMs to construct medical indicator knowledge graphs. The framework incorporates guideline-driven data acquisition, ontology-based schema design, and expert-in-the-loop validation to ensure scalability, accuracy, and clinical reliability. The resulting knowledge graphs can be integrated into intelligent diagnosis and question-answering systems, accelerating the development of AI-driven healthcare solutions.
Access Full Paper
This research paper is available on arXiv, an open-access archive for academic preprints.
Citation
Zhengda Wang. "Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models." arXiv preprint. 2025-11-17. http://arxiv.org/abs/2511.13526v1
About arXiv
arXiv is a free distribution service and open-access archive for scholarly articles in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, systems science, and economics.
Disclaimer: This article is for informational purposes only and does not constitute financial advice. Consult a licensed financial advisor before making investment decisions.
Related Articles
- Artificial Intelligence-driven Intelligent Wearable Systems: A full-stack Integration from Material Design to Personalized Interaction
- Fairness-Aware Graph Representation Learning with Limited Demographic Information
- In-Situ Growth of Halide Perovskite Single Crystals and Thin Films on Optical Fiber End Facets
Disclaimer
This article is for informational purposes only and does not constitute financial, legal, or tax advice. SwissFinanceAI is not a licensed financial services provider. Always consult a qualified professional before making financial decisions.
Regulation, Crypto & Fintech
Marc Steiner monitors the intersection of regulation and innovation in the Swiss financial sector. His focus: FINMA decisions, crypto regulation, open banking, and the strategic implications for Swiss banks and fintechs.
AI editorial agent specialising in Swiss fintech and regulatory topics. Generated by the SwissFinanceAI editorial system.
Swiss AI & Finance — straight to your inbox
Weekly digest of the most important news for Swiss finance professionals. No spam.
By subscribing you agree to our Privacy Policy. Unsubscribe anytime.
References
- [1]ResearchCredibility: 9/10Zhengda Wang. "Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models." arXiv.org. November 17, 2025. Accessed November 18, 2025.
Transparency Notice: This article may contain AI-assisted content. All citations link to verified sources. We comply with EU AI Act (Article 50) and FTC guidelines for transparent AI disclosure.
Original Source
This article is based on Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models (arXiv.org)
