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大型语言模型驱动的知识图构建在败血症护理中使用多中心临床数据库:开发和可用性研究

Hao Yang^1,2,3, Jiaxi Li⁴, Chi Zhang¹

¹Department of Critical Care Medicine, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Institutes for Systems Genetics, Sichuan University, West China Hospital, Chengdu, China.

Journal of medical Internet research

|March 27, 2025

概括

像GPT-4.0这样的大型语言模型 (LLM) 可以从复杂的临床数据中构建全面的败血症知识图. 这种方法提高了败血症的理解和临床决策,为医学研究制定了新的标准.

科学领域:

医疗信息学医疗信息学
人工智能在医学中的应用
临床数据科学临床数据科学

背景情况:

败血症呈现出显著的异质性和数据复杂性,挑战了传统的知识图结构.
大型语言模型 (LLM) 提供了一种新的方法来整合和分析非结构化临床数据,以改善败血症管理.

研究的目的:

使用GPT-4.0和多中心临床数据库开发一个全面的败血症知识图.
提高对败血症的理解,并为临床决策提供可操作的见解.
建立一个多中心的败血症数据库 (MSD),以支持知识图的开发.

主要方法:

收集了三家医院 (10,544名败血症患者) 的临床指南,公共数据库和现实数据.
雇佣了GPT-4.0与先进的提示工程实体识别和关系提取.
构建了一个细微的败血症知识图,整合了各种数据来源.

主要成果:

建立了一个败血症数据库,包含10544名患者记录.
在九个实体概念中开发了一个毒性知识图,其中有1894个节点和2021个关系.
与其他模型相比,GPT-4.0在实体识别和关系提取方面获得了较高的F1分数 (76.76%在败血症数据上,65.42%在CMeEE数据集上).

结论:

开创性地使用LLMs (GPT-4.0) 来构建综合性败血症知识图.
先进的提示工程和多中心数据集成提高了效率和准确性.
败血症知识图为临床决策和未来研究提供了一个强大的框架.

关键词:

在 GPT-4.0 中使用.知识图表知识图表大型语言模型.快速的工程迅速的工程现实世界的真实世界.这是一种血症.

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