基于众包的药物副作用知识图的构建,使用大语言模型与对Semaglutide的应用
Zhijie Duan1, Kai Wei2, Zhaoqian Xue3
1University of Pennsylvania, Philadelphia, PA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
大型语言模型 (LLM) 从社交媒体中提取药物的副作用,为药物监测创建知识图表. 这种方法提供了以患者为中心的关于塞马格卢提德副作用的见解,补充了现有的安全数据.
科学领域:
- 药监和药物安全 药监和药物安全
- 医疗保健中的人工智能
- 社交媒体数据挖掘
背景情况:
- 社交媒体为药物监督提供了有价值的真实世界患者体验数据.
- 从杂的社交媒体内容中提取结构化信息是一项挑战.
研究的目的:
- 通过使用大语言模型 (LLM) 来从社交媒体中提取药物的副作用,提出一个系统的框架.
- 将提取的数据组织成知识图 (KG) 以进行全面分析.
- 用Reddit数据调查减肥的塞马格卢提德副作用.
主要方法:
- 开发了一个利用LLM的框架,从社交媒体中自动提取药物副作用.
- 将框架应用于来自Reddit的semaglutide数据,构建一个知识图.
- 分析了不同品牌和随着时间的推移报告的副作用.
- 通过与FAERS数据库进行比较,验证了调查结果.
主要成果:
- 从社交媒体数据中成功地提取和结构化药物副作用,使用LLMs.
- 创建了一个知识图,详细介绍了在Reddit上报道的塞马格卢提德副作用.
- 确定了以患者为中心的关于赛马格卢提德安全性概况的见解,补充了FAERS数据.
结论:
- 法律学可以有效地将非结构化的社交媒体数据转化为用于药物监管的结构化知识图.
- 这一框架为药物副作用提供了以患者为中心的有价值的见解,增强了安全监测.
- 该方法证明了在药物监测和现实世界证据生成中更广泛的应用的可行性.
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