加强疫苗安全监测:利用微调的大型语言模型,从急诊部的分离笔记中提取疫苗的提及
Sedigh Khademi1, Jim Black2, Christopher Palmer1
1Murdoch Children's Research Institute, Parkville, Australia.
Studies in health technology and informatics
|August 8, 2025
概括
精心调整的Llama 3模型准确地从紧急备忘录中提取疫苗信息,用于安全监测. 这种方法支持在免疫接种后早期检测不良事件,增强公共卫生.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 疫苗安全监督 疫苗安全监督
背景情况:
- 紧急部门的分类笔记包含了对公共卫生的有价值数据.
- 手动提取疫苗信息是耗时且资源密集的.
- 需要自动化方法来有效监测疫苗的安全性.
研究的目的:
- 评估精心调整的Llama 3模型,从紧急部门的分类笔记中提取与疫苗有关的信息.
- 将大型语言模型的性能与基于规则的方法进行比较.
- 评估在资源有限的环境中部署这些模型的可行性.
主要方法:
- 利用提示工程从分拣笔记创建一个标记的数据集.
- 在标记的数据集上微调的Llama 3.2模型.
- 微调模型,提示性工程模型和基于规则的系统的性能比较.
- 采用模型量化来实现高效的部署.
主要成果:
- 微调的拉玛30亿参数模型在提取疫苗名称方面表现出卓越的准确性.
- 量子化促进了在资源有限的环境中高效部署.
- 该研究证实了LLM在自动化数据提取方面的潜力.
结论:
- 精心调整的大型语言模型显示出对自动化疫苗安全监测的重大前景.
- 这项技术可以支持近乎实时检测免疫接种后的不良事件.
- 从临床笔记中自动提取数据增强了公共卫生监测能力.
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