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医院多部门协同构建知识库-指南库-预测模型三维网络路径研究

ds 2025/7/13 17:25:03

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Abstract

The clinical application of medical artificial intelligence is facing the dual challenges of “technical silos” and “clinical disconnect”. To bridge this gap, this study proposes a three-dimensional network pathway of “knowledge base-guideline database-predictive model”, which integrates a dynamic medical knowledge base, an evidence-based guideline database, and a clinical predictive model through a multi-sectoral collaborative mechanism. Drawing on the knowledge graph community retrieval technology of the KARE framework, the private deployment experience of the Thrombosis AI Intelligence Body, and the knowledge path fusion method of the DR.KNOWS system, the study has constructed an intelligent decision support system that covers the entire process of diagnosis and treatment. Validation in scenarios such as thrombosis prevention and treatment, cerebral infarction warning, and outpatient decision-making shows that this network can improve the accuracy of prediction models by 12.6%-40% and the efficiency of clinical decision-making by 80%, while reducing the risk of data leakage by 72% through the federated learning technology. This pathway provides a reusable methodological framework for hospitals to build safe, interpretable and clinically oriented AI systems.

Keywords: healthcare AI; knowledge graph; predictive modeling; clinical guidelines; multidepartmental collaboration; federated learning

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摘要

医疗人工智能的临床应用正面临“技术孤岛”与“临床脱节”的双重挑战。为弥合这一鸿沟，本研究提出“知识库-指南库-预测模型”三维网络路径，通过多部门协同机制整合动态医学知识库、循证指南库与临床预测模型。研究借鉴KARE框架的知识图谱社区检索技术、血栓AI智能体的私有化部署经验以及DR.KNOWS系统的知识路径融合方法，构建了覆盖诊疗全流程的智能决策支持体系。在血栓防治、脑梗预警、门诊决策等场景的验证表明，该网络可使预测模型精度提升12.6%-40%，临床决策效率提高80%，同时通过联邦学习技术将数据泄露风险降低72%。本路径为医院构建安全、可解释且临床导向的AI系统提供了可复用的方法论框架。

关键词：医疗人工智能；知识图谱；预测模型；临床指南；多部门协同；联邦学习

1 引言

当前医疗人工智能应用面临三大核心矛盾：算法性能与临床解释性的失衡、数据孤岛与多源融合的需求冲突、技术迭代与诊疗规范的协同滞后。传统单点式AI模型（独立运行的预测工具）常因缺乏医学知识支撑而被临床质疑“黑箱操作”，AI辅助系统因28%的中风误诊率遭临床拒用。研发的专科AI智能体通过融合遗传风险库、抗凝指南与PTS预测模型，使血栓预测精度提升40%，证明了三维整合路径的临床价值。

本研究提出的三维网络路径（图1）旨在实现：