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KG4Diagnosis:一种基于知识图谱增强的医疗诊断分层多智能体大语言模型框架

1 University of Warwick
2 Cranfield University
3 University of Cambridge

Abstract

将大语言模型(LLMs)应用于医疗诊断领域,需要搭建系统化框架,以此应对复杂的临床诊疗场景,同时保障专业诊疗能力。本文提出 KG4Diagnosis 这一全新的分层多智能体框架,该框架融合大语言模型与自动化知识图谱构建技术,覆盖多个医学专科领域的 362 种常见疾病。本框架依托双层架构复刻真实医疗诊疗体系:由全科医生智能体完成初诊与分诊工作,再联动专科智能体,针对特定病症开展深度诊断。该研究的核心创新在于自研端到端知识图谱生成方法,具体包含三方面内容:(1)适配医学术语、基于语义驱动的实体与关系抽取技术;(2)从非结构化医学文本中重构多维决策关联关系;(3)结合人工引导推理实现知识拓展。KG4Diagnosis 可作为可拓展底座,搭建各类专业化医疗诊断系统,能够新增收录疾病病种与医学相关知识。框架采用模块化设计,可无缝对接各类领域专项优化功能,助力研发精准化、定制化的医疗诊断系统。本文还给出配套架构规范与实施准则,便于该框架在各类医疗场景中落地应用。

How to Cite

Zuo, K., Jiang, Y., & Mo, F. (2026). KG4Diagnosis:一种基于知识图谱增强的医疗诊断分层多智能体大语言模型框架. 亚洲社会创新与发展期刊, 2(1), 10. 取读于 从 https://ajsid.org/index.php/pub/article/view/30

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