摘要
目的通过研究基于大语言模型的患者病历药物警戒画像技术,旨在提高我国药品上市后安全性监测评价的效率和准确性,为保障广大患者的用药安全提供科学方法和技术支持。方法构建包含患者个体差异、用药细节和不良反应表现的药物警戒画像,利用药物警戒领域知识图谱对大语言模型进行增强,并设计针对性的提示词来引导模型输出药物警戒画像。结果大语言模型在主动监测中展现出显著优势,能够有效处理和分析医疗文本数据,提高药品不良反应监测和预测能力。通过提示词的设计,模型能够更加准确地描绘患者药物警戒画像,为医疗专业人员提供决策支持。结论基于大语言模型的患者病历药物警戒画像技术研究为早期发现和预防药品不良反应的发生提供了科学依据和技术支持,有助于降低医疗成本,改善医疗结局或预后,为保障患者用药安全开辟了新的路径。
Objective To enhance the efficiency and accuracy of post-marketing safety monitoring and evaluation of drugs in China by studying large language models-based patient medical record pharmacovigilance profiling techniques,providing scientific methods and technical support to ensure the safe use of drugs for patients.Methods This study constructs a pharmacovigilance profile that includes individual patient differences,medication details,and adverse reaction manifestations.It enhances a large language model with a knowledge graph in the field of pharmacovigilance and designs targeted prompts to guide the model to output pharmacovigilance profiles.Results Large language models demonstrate significant advantages in active monitoring,effectively processing and analyzing medical text data,and improving the monitoring and prediction capabilities of drug adverse reactions.Through the design of prompts,the model can more accurately depict patient pharmacovigilance profiles,providing decision support for medical professionals.Conclusions The study of large language model-based patient medical record pharmacovigilance profiling technology provides scientific evidence and technical support for the early detection and prevention of drug adverse reactions,helping to reduce medical costs,improve medical outcome prognoses,and opens new paths to ensure patient drug safety.
作者
吴正善
张纾
林翼旻
雷毅
王青
孙志刚
张琳
WU Zhengshan;ZHANG Shu;LIN Yimin;LEI Yi;WANG Qing;SUN Zhigang;ZHANG Lin(Fujian Center for Drug Evaluation and Monitoring,Fuzhou 350003,China;Research Center for Pharmacovigilance IT and Data Science,Cross-strait Tsinghua Research Institute,Xiamen 361006,China)
出处
《医药导报》
北大核心
2025年第4期554-560,共7页
Herald of Medicine
基金
国家药品监督管理局药品监管科学体系建设重点项目(RS2024G001)
药品监管科学全国重点实验室课题(2024SKLDRS0233)。
关键词
药物警戒
主动监测
患者画像
大语言模型
知识图谱
Pharmacovigilance
Active monitoring
Patient profiling
Large language models
Knowledge graph
