利用GPT-4进行食品效应总结,通过代提示来加强产品特定指导的开发
Yiwen Shi1, Ping Ren2, Jing Wang2
1College of Computing and Informatics, Drexel University, Philadelphia, PA, United States.
Journal of biomedical informatics
|November 2, 2023
概括
使用GPT-4的代提示进行自动化食品效应总结,显著提高了新药申请评估的准确性,加速了仿制药的开发.
科学领域:
- 药理动力学和药物开发
- 在监管科学中的自然语言处理 (NLP)
背景情况:
- 来自新药申请 (NDA) 的食物效应总结对于制定生物等价性研究指南至关重要.
- 手动总结是耗时的,需要自动化解决方案.
- 像GPT-4这样的大型语言模型 (LLM) 对这一特定任务的有效性需要研究.
研究的目的:
- 开发和评估一种自动化方法,从NDA文件中总结食物效应信息.
- 为此任务评估使用LLM (ChatGPT和GPT-4) 代提示的性能.
- 为了确定LLM生成的摘要的准确性和效率,用于产品特定指导 (PSG) 开发.
主要方法:
- 为ChatGPT和GPT-4开发了一种三转代提示方法.
- 用关键字和长度控制的提示来改进摘要.
- 评估包括自动化指标,FDA专业人员以及对100份NDA文件的GPT-4自我评估.
主要成果:
- 代提示逐步提高了摘要质量.
- GPT-4的表现优于ChatGPT,FDA专业人员的评分更高 (43%对12%) 和GPT-4 (64%对35%).
- GPT-4生成的摘要显示了高的事实一致性 (FDA专业人员的85%,GPT-4的72%.
结论:
- GPT-4显示出编制准确的食品效应总结的巨大潜力.
- 这项技术可以提高PSG评估的效率,并加速仿制药的开发.
- 代提示是优化法学士在法规文件总结中的性能的一个有效策略.
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