用知识图谱增强大模型，分层协作诊断疾病
KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis
- 分两层：全科代理初筛，专科代理深入诊断
- 自动构建涵盖362种疾病的医疗知识图谱
- 适合想快速搭建专业诊疗系统的开发者
将大语言模型引入医疗诊断需系统化框架以应对复杂场景并保持专业性。我们提出KG4Diagnosis，一种新型分层多智能体框架，融合大模型与自动化知识图谱构建，覆盖362种常见疾病及多个医学专科。该框架采用双层架构：全科医生代理负责初步评估与分诊，协调专科代理在特定领域进行深入诊断。核心创新在于端到端的知识图谱生成方法，包括：(1) 针对医学术语优化的语义驱动实体与关系抽取；(2) 从非结构化医学文本重构多维决策关系；(3) 人工引导的推理实现知识扩展。该框架可作为专业化医疗诊断系统的可扩展基础，支持新疾病与新知识的持续融入。其模块化设计便于集成领域特定增强，适用于开发针对性医疗诊断系统。我们提供架构指南与实施协议，助力跨医疗场景落地。
原文摘要 · Abstract (English)
Integrating Large Language Models (LLMs) in healthcare diagnosis demands systematic frameworks that can handle complex medical scenarios while maintaining specialized expertise. We present KG4Diagnosis, a novel hierarchical multi-agent framework that combines LLMs with automated knowledge graph construction, encompassing 362 common diseases across medical specialties. Our framework mirrors real-world medical systems through a two-tier architecture: a general practitioner (GP) agent for initial assessment and triage, coordinating with specialized agents for in-depth diagnosis in specific domains. The core innovation lies in our end-to-end knowledge graph generation methodology, incorporating: (1) semantic-driven entity and relation extraction optimized for medical terminology, (2) multi-dimensional decision relationship reconstruction from unstructured medical texts, and (3) human-guided reasoning for knowledge expansion. KG4Diagnosis serves as an extensible foundation for specialized medical diagnosis systems, with capabilities to incorporate new diseases and medical knowledge. The framework's modular design enables seamless integration of domain-specific enhancements, making it valuable for developing targeted medical diagnosis systems. We provide architectural guidelines and protocols to facilitate adoption across medical contexts.
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