利用大型语言模型从处方药标签中提取药物安全信息
Undina Gisladottir1, Michael Zietz1,2, Sophia Kivelson2
1Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Drug safety
|September 2, 2025
概括
生成语言模型可以有效地从产品标签中提取药物安全信息,与以前的方法相匹配或超越. 这项技术有助于识别药物不良反应和相互作用,改善药物安全性研究.
科学领域:
- 自然语言处理 (NLP)
- 机器学习
- 药物监督管理
背景情况:
- 药物不良反应和药物相互作用是致病率和死亡率的重要原因.
- 结构化产品标签 (SPL) 是药物安全数据的关键来源.
- 使用传统的NLP方法从SPL中提取安全信息是一项挑战.
研究的目的:
- 评估生成语言模型 (LLM) 来从SPL中提取药物安全信息.
- 将不同LLM与基线方法的性能进行比较.
- 评估LLM用于获取药物相互作用数据的适应性.
主要方法:
- 将GPT,Llama和Mixtral LLMs与从SPL中提取不良反应的两个基线方法进行比较.
- 研究了提示策略和术语复杂性对AR提取的影响.
- 评估生成模型在没有微调的情况下提取药物相互作用的能力.
主要成果:
- 在没有额外培训的情况下,生成型LLM,特别是GPT-4实现了与最先进型号相比或更好的性能.
- 提取性能受到SPL段落,上下文和AR术语复杂性的影响.
- 通过从药物相互作用部分提取药物名称来证明模型的概括性.
结论:
- 产生性LLM显示出自动化从SPL中提取药物安全信息的巨大潜力.
- 这种自动化可以加强市场后监测,并有助于减少不良反应.
- 未来的研究应该完善提示并扩展复杂安全数据的模型功能.
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