Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models

TL;DR

This paper introduces an automated framework that leverages retrieval-augmented large language models to construct structured medical indicator knowledge graphs, enhancing clinical decision support and reducing manual curation.

Contribution

It presents a novel integration of retrieval-augmented generation with LLMs, ontology-based schema design, and expert validation for scalable, accurate medical knowledge graph construction.

Findings

Automated framework effectively constructs medical indicator knowledge graphs.

Knowledge graphs improve clinical decision support systems.

Framework reduces reliance on manual curation and rule-based extraction.

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.