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

2025-11-17

AI Summary

Current clinical knowledge graph construction heavily relies on manual curation and rule-based approaches, struggling to handle the semantic complexity and contextual ambiguity inherent in clinical guidelines and biomedical literature—resulting in low automation and insufficient clinical reliability of structured, interoperable medical indicator knowledge graphs. To address this, we propose a guideline-driven, ontology-guided, retrieval-augmented generation (RAG) and large language model (LLM)-integrated framework for automated knowledge graph construction. Our method synergistically combines domain ontology modeling, dynamic multi-source guideline retrieval, structured schema generation, and expert-in-the-loop validation. It significantly improves the accuracy, scalability, and clinical consistency of knowledge extraction. We experimentally constructed a high-quality knowledge graph covering 200+ core medical indicators and empirically validated its effectiveness across downstream tasks—including intelligent diagnosis and treatment, clinical decision support, and medical question answering.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Ontologies
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Automating medical knowledge graph construction using retrieval-augmented LLMs
• Overcoming manual curation limitations in clinical knowledge graphs
• Enabling structured clinical decision support through automated KG generation

Innovation

• Automated framework combines RAG with large language models
• Integrates guideline-driven data acquisition and ontology design
• Uses expert-in-the-loop validation for clinical reliability