语言模型如何帮助药品制造偏差和调查?
Hossein Salami1, Brandye Smith-Goettler2, Vijay Yadav2
1Digital Services, MMD, Merck & Co., Inc., 126 E. Lincoln Ave., Rahway, NJ 07065, USA.
International journal of pharmaceutics
|December 19, 2024
概括
大型语言模型 (LLM) 通过从偏差报告中提取知识并识别类似事件,在制药生产中显示出前景. 人工智能工具可以提高效率和合规性,尽管需要进一步的研究.
科学领域:
- 人工智能的人工智能
- 制药制造业 制药制造业 制药制造业
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在各种自然语言处理任务中表现强.
- 为特定领域的应用,特别是制药制造业,利用LLM是一个公开的挑战.
- 历史制造偏差记录通常是无结构的和未充分利用的.
研究的目的:
- 调查生成LLM和文本嵌入模型对分析制药制造偏差的实用性.
- 评估AI在提高制药制造业生产率,效率,质量和合规性方面的潜力.
- 探索自动化知识提取和偏差报告的相似性分析.
主要方法:
- 构建一个行业相关的药品制造偏差数据集.
- 从偏差报告中自动提取知识的生成LLM (例如GPT,Claude) 的评估.
- 评估使用矢量空间分析识别类似历史事件的文本嵌入模型.
主要成果:
- 生成型LLM实现了高度精确的实体提取,更大的模型显示出卓越的性能.
- 在观察到的幻觉行为之外,LLM表现出强大的推理能力.
- 嵌入模型在捕捉特定偏差类别内的语义相似性方面表现出有效性.
结论:
- 人工智能驱动的工具,特别是LLM,在提高制药制造工作流程方面具有重大潜力.
- 对偏差报告的自动分析可以改善案例管理,并降低新制造活动的风险.
- 进一步的研究是必要的,以解决LLM应用的细微差别,并优化AI在这个领域的整合.
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