药监中的语言模型:应用,承诺和限制
Francesco Salvo1, Lelio Crupi2, Clément Cholle3
1Inserm, BPH, Team AHeaD, Inserm U1219, université de Bordeaux, 33000 Bordeaux, France; Service de pharmacologie médicale, pôle de Santé publique, centre régional de pharmacovigilance de Bordeaux - DROM, CHU de Bordeaux, 33000 Bordeaux, France.
语言模型通过自动化诸如药物不良反应提取等任务来提高药监效率. 然而,透明度和监管接受等挑战需要谨慎,跨学科的实施,以实现可持续的系统.
科学领域:
- 药物监督和人工智能 药物监督和人工智能
- 药物安全中的自然语言处理.
背景情况:
- 语言模型有可能提高药物监督质量和效率.
- 传统的NLP和先进的语言模型在药物安全工作流程中具有多种应用.
- 目前,实际约束限制了这些技术的广泛采用.
研究的目的:
- 检查传统NLP和先进语言模型在药监中的应用.
- 分析它们在药物不良反应提取,病例处理和证据查中的应用.
- 确定整合这些技术的挑战和机会.
主要方法:
- 在PubMed®上进行了系统的文献搜索.
- 基于专家的选择和参考选完善了文献.
- 分析重点是传统的NLP技术和先进的语言模型.
主要成果:
- 传统的NLP方法为结构化数据提取提供了透明度,但与临床叙述的可变性作斗争.
- 先进的语言模型擅长于对非结构化文本的上下文理解,但面临监管障碍 (例如幻觉,透明度).
- 大型语言模型具有显著的计算和环境成本.
结论:
- 自然语言处理和高级语言模型是药物监督的补充工具.
- 整合需要跨学科的合作,人类的监督和可持续的实施策略.
- 均衡的方法可以提高药物监督的可扩展性,可信度和临床完整性.
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