摘要
Objectives:Chemotherapy-induced nausea and vomiting(CINV)is a common adverse effect among breast cancer patients,significantly affecting quality of life.Existing evidence on the prevention,assessment,and management of this condition is fragmented and inconsistent.This study constructed a CINV knowledge graph using a large language model(LLM)to integrate nursing and medical evidence,thereby supporting systematic clinical decision-making.Methods:A top-down approach was adopted.1)Knowledge base preparation:Nine databases and eight guideline repositories were searched up to October 2024 to include guidelines,evidence summaries,expert consensuses,and systematic reviews screened by two researchers.2)Schema design:Referring to the Unified Medical Language System,Systematized Nomenclature of Medicine-Clinical Terms,and the Nursing Intervention Classification,entity and relation types were defined to build the ontology schema.3)LLM-based extraction and integration:Using the Qwen model under the CRISPE framework,named entity recognition,relation extraction,disambiguation,and fusion were conducted to generate triples and visualize them in Neo4j.Four expert rounds ensured semantic and logical consistency.Model performance was evaluated using precision,recall,F1-score,and 95%confidence interval(95%CI)in Python 3.11.Result:A total of 47 studies were included(18 guidelines,two expert consensuses,two evidence summaries,and 25 systematic reviews).The Qwen model extracted 273 entities and 289 relations;after expert validation,238 entities and 242 relations were retained,forming 244 triples.The ontology comprised nine entity types and eight relation types.The F1-scores for named entity recognition and relation extraction were 82.97(95%CI:0.820,0.839)and 85.54(95%CI:0.844,0.867),respectively.The average node degree was 2.03,with no isolated nodes.Conclusion:The LLM-based CINV knowledge graph achieved structured integration of nursing and medical evidence,offering a novel,data-driven tool to support clinical nursing decision-making and advance intelligent healthcare.
目的化疗相关恶心呕吐(Chemotherapy-Induced Nausea and Vomiting,CINV)是乳腺癌患者最常见且影响生活质量的不良反应之一。现有关于CINV的预防、评估、治疗等内容分散于多种文献中,缺乏系统整合与结构化呈现,限制了其临床应用。为此,该研究利用大语言模型构建乳腺癌患者CINV知识图谱,系统整合护理与医学证据,为临床决策提供智能化辅助作用。方法采用自顶向下的方法构建乳腺癌患者CINV的知识图谱。构建过程:(1)知识库构建:系统检索建库至2024年10月的9个数据库和8个指南库,纳入指南、证据总结、专家共识及系统评价,由两名研究者独立筛选后形成知识库。(2)模式设计:参考统一医学语言系统、医学术语系统命名法-临床术语及护理措施分类体系,自顶向下定义实体与关系类型,构建本体框架。(3)基于Qwen模型的知识抽取与融合:利用Qwen模型按CRISPE框架完成实体命名识别、关系抽取、实体消歧与融合,生成三元组,并导入Neo4j实现可视化。通过四轮专家会议,核对实体边界、关系类型与语义一致性,确保提取结果的准确性与逻辑性。采用精确率、召回率、F1值和95%可信区间评估Qwen模型在实体命名识别与关系抽取中的表现和结果稳定性。结果共纳入47篇研究文献(18篇指南,2篇专家共识,2篇证据总结,25篇系统综述)。使用Qwen模型自动化抽取和消歧后获得273个实体和289条关系,专家核实后保留238个实体和242条关系,融合构成244组三元组,图谱本体模式层包括9个实体类型和8个关系类型。模型在命名实体识别与关系抽取中F1值分别达到82.9(95%CI:0.820,0.839)和85.54(95%CI:0.844,0.867)。图谱中各节点平均连接度为2.03,无孤立节点。结论基于大语言模型构建的CINV知识图谱实现了护理与医学证据的结构化整合,为临床护理决策提供了新型的数据驱动工具,促进智慧化护理的发展。
基金
supported by Education and Research Project of Fujian Province Young and Middle-aged Teachers(JAT241035)
High-level Talent Project of Fujian Medical University(XRCZX2024036)。
