目的 构建基于知识图谱的ICU呼吸机警报管理智能问答系统，提高医护人员对呼吸机警报的识别和处理能力，确保患者安全和治疗效果。方法 整合学术论文、指南、标准、临床实践案例、设备用户手册等多种来源知识，通过专家访谈和专家会议制订知识框架。采用自动化和人工审核相结合的方式进行实体构建、知识抽取、融合，以“实体-关系-实体”三元组数据输入Neo4j图数据库进行存储。并针对自然语言问题进行语义理解和解析，创建知识图谱Cypher查询语句，使知识图谱具有智能问答功能。采用系统可用性量表进行问答系统可用性测试。结果 构建的知识图谱涵盖通气模式、参数设置、警报类型、警报原因、处理措施、设备维护6个领域，包含206个知识实体、15种实体间关系及256条知识点的联接，并基于知识图谱开发了Web端智能问答系统。系统可用性量表得分为（76.44±13.17）分。结论 该研究应用知识图谱构建的呼吸机警报管理智能问答系统具有较好的科学性和可用性。通过知识体系可视化、智能问答、个性化推荐等功能，为医护人员提供了有效识别、精准解除警报的辅助决策支持工具，有利于提高临床警报安全。

Objective To construct a knowledge graph based intelligent Q&A system for ventilator alarm management in intensive care units，to improve healthcare professionals’ ability to recognize and handle ventilator alarms，and to enhance patient safety and treatment outcomes. Methods We integrated knowledge from various sources such as thesis，guidelines，standards，clinical practice cases，device user manuals，etc.，and developed the knowledge framework through expert interviews and expert meetings. We used a combination of automation and manual review to construct entities，extract and integrate knowledge，and input data into the Neo4j graph database as “entity-relationship-entity” ternary data for storage. And for the semantic understanding and parsing of natural language questions，the knowledge graph Cypher query statement was created，so that the knowledge graph has intelligent question and answer functions. The system usability scale was used to test the usability of the Q&A system. Results The constructed knowledge graph covered six fields，including ventilation mode，parameter settings，alarm types，alarm causes，treatment measures，and equipment maintenance，and contains 206 knowledge entities，15 kinds of inter-entity relationships，and 256 linkages of knowledge points，and a web-side intelligent Q&A system has been developed based on the knowledge graph. The mean score of the System Usability Scale was （76.44 ± 13.17）. Conclusion The knowledge graph based intelligent Q&A system for ventilator alarm management constructed in this study has certain scientific validity and usability. Through the visualization of knowledge system functions，intelligent question and answer，and personalized recommendation，it provides an auxiliary decision support tool for healthcare professionals to effectively identify and accurately deactivate the alarms，and improves the safety of clinical alarms.