用检索增强大模型自动构建医疗指标知识图谱，提升临床决策支持效率。
Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models
- 结合检索增强生成与医学指南，自动提取结构化医疗信息。
- 通过专家参与验证，确保知识图谱的准确性与临床可靠性。
- 适合医疗AI研发者用于加速智能诊断系统开发。
人工智能正在重塑现代医疗，推动疾病诊断、治疗决策和生物医学研究的进步。大型语言模型在从复杂医疗文本中提取深层知识和进行语义推理方面发挥重要作用。然而，有效的临床决策支持需要结构化、可互操作的知识形式。知识图谱通过将异构医疗信息整合为语义一致的网络来满足这一需求。当前的临床知识图谱仍严重依赖人工标注和规则提取，受限于医学指南和文献的复杂性与上下文模糊性。为此，我们提出一种自动化框架，结合检索增强生成（RAG）与大语言模型，构建医疗指标知识图谱。该框架包含指南驱动的数据获取、基于本体的模式设计以及专家参与的闭环验证，确保可扩展性、准确性和临床可靠性。生成的知识图谱可集成至智能诊断与问答系统，加速AI驱动医疗解决方案的发展。
原文摘要 · Abstract (English)
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.
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