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开发一种基于化学疗法诱导的乳腺癌恶心和吐的大型语言模型知识图及其对护理的影响

Yu Liu¹, Jingjing Chen¹, Xianhui Lin²

¹School of Nursing, Fujian Medical University, Fuzhou, Fujian, China.

International journal of nursing sciences

|December 10, 2025

概括

使用大型语言模型 (LLM) 构建了一个知识图表,以整合乳腺癌患者化疗诱导的恶心和吐 (CINV) 的证据. 该工具通过组织碎片化的医疗和护理信息来支持临床决策.

科学领域:

瘤科护理护理专业
医疗信息学医疗信息学
医疗保健中的人工智能

背景情况:

化疗诱导的恶心和吐 (CINV) 是乳腺癌患者普遍和令人痛苦的副作用,严重损害了他们的生活质量.
关于CINV预防,评估和管理的现有临床证据往往是分散的,缺乏一致性,阻碍了有效的临床决策.
需要一个系统的方法来整合多样化的医疗和护理知识进行CINV管理至关重要.

研究的目的:

通过整合碎片化的护理和医学证据,构建化疗诱导的恶心和吐 (CINV) 的综合知识图.
利用大型语言模型 (LLM) 来系统地提取和组织CINV相关信息.
开发一个数据驱动的工具,支持系统的临床护理决策,用于CINV管理.

主要方法:

采用了自上而下的方法,包括通过广泛的文献搜索 (数据库和指导方针到2024年10月) 准备知识库.
设计了一个本体学模式,引用既定的医疗和护理术语 (UMLS,SNOMED CT,NIC) 来定义实体和关系类型.
在CRISPE框架内,Qwen LLM被用于命名实体识别,关系提取,明确和融合,以生成知识图的三倍,在Neo4j中可视化并由专家验证.

主要成果:

该研究包括47项相关研究,最终形成了一个知识图,其中包含238个验证的实体和242个关系,形成244个三重组,分为9个实体和8个关系类型.
该LLM在命名实体识别 (F1得分:82.97%) 和关系提取 (F1得分:85.54%) 中表现出强的表现,表明有效的知识提取.
由此产生的知识图表表现出良好的连接性,平均节点程度为2.03,没有孤立的节点,确保了全面的数据集成.

结论:

开发的基于LLM的CINV知识图成功将不同的护理和医疗证据集成到一个结构化的格式中.
这种新的,数据驱动的知识图表作为一种有价值的工具,可以增强用于CINV管理的系统临床护理决策.
该研究强调了LLM在通过为复杂的临床挑战创造结构化知识资源来推进智能医疗保健方面的潜力.

关键词:

乳腺癌是什么? 乳腺癌是什么化疗引起的恶心和吐.知识图表知识图表大型语言模型.症状管理症状管理

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