KG4Diagnosis：一种基于知识图谱增强的层级多智能体LLM框架用于医学诊断
KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis
- University of Warwick（华威大学）
- Cranfield University（克兰菲尔德大学）
- University of Cambridge（剑桥大学）
- University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
KG4Diagnosis提出层级多智能体LLM框架，结合自动化知识图谱构建，覆盖362种疾病，通过全科与专科智能体协作，实现可扩展的医学诊断。
AI中文摘要：
将大型语言模型（LLMs）整合到医疗诊断中，需要能够处理复杂医疗场景并保持专业专长的系统性框架。我们提出了KG4Diagnosis，一种新颖的层级多智能体框架，它将LLMs与自动化知识图谱构建相结合，涵盖各医学专科的362种常见疾病。我们的框架通过两层架构模拟真实世界的医疗系统：一个全科医生（GP）智能体负责初步评估和分诊，并与专科智能体协调，在特定领域进行深入诊断。核心创新在于我们的端到端知识图谱生成方法，包括：（1）针对医学术语优化的语义驱动实体和关系抽取，（2）从非结构化医学文本中重构多维决策关系，以及（3）人类引导的推理用于知识扩展。KG4Diagnosis作为专科医学诊断系统的可扩展基础，具备纳入新疾病和医学知识的能力。该框架的模块化设计能够无缝集成领域特定的增强功能，使其对于开发针对性的医学诊断系统具有重要价值。我们提供了架构指南和协议，以促进其在各种医疗场景中的采用。
英文摘要：
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.
