通过使用识别问题涉及系统来确定制药行业监管文件的问题相似性:评估研究
Nidhi Saraswat1, Chuqin Li1, Min Jiang1
1Eli Lilly and Company, Indianapolis, IN, United States.
JMIR AI
|June 14, 2024
概括
人工智能 (AI) 可以在制药监管事务中识别类似的问题,提高效率. 临床BERT模型达到90.66%的准确性,在识别语义问题相似性方面表现优于其他模型.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 监管事务 (RA) 部门面临着从多种搜索系统中提取和总结信息的挑战.
- 在RA中,手动提取信息的过程是耗时的,需要对细节的仔细关注.
- 开发基于人工智能的智能搜索系统对于减少手工工作和提高RA部门结果质量至关重要.
研究的目的:
- 开发和评估基于人工智能的计算模型,用于在监管事务领域内自动识别语义上相似的问题对.
- 评估基于参与的识别问题系统对监管问题相似性检测的有效性.
主要方法:
- 利用基于变压器的模型的转移学习技术,包括来自变压器的双向编码器表示 (BERT),临床BERT,BioBERT和BlueBERT.
- 在各种身体上预训练的模型上进行了实验.
- 评估模型性能使用手动标记的数据集150个问题对特定于制药监管领域.
主要成果:
- 与其他特定领域的BERT模型相比,临床BERT模型在确定RA领域内的问题相似性方面表现优异.
- 当BERT模型与足够的临床领域问题对进行微调时,它通过转移学习展示了通过转移学习来学习特定领域知识的最佳能力.
- 性能最高的模型在测试数据集上实现了90.66%的准确性.
结论:
- 经过预先训练的语言模型可以有效地识别制药监管领域的问题相似性.
- 在临床笔记上预训练的基于变压器的模型在此任务中优于在一般生物医学文本上预训练的模型.
- 数据增强技术,如逆向翻译和实体替换,并没有提高性能,这可能是由于监管文本的专业性. 建议进一步研究将先进的语言模型应用于监管文件.
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