在监管文件中使用人工智能将自由文本分类为预定义的部分:对药物标签文件的案例研究
Magnus Gray1, Joshua Xu1, Weida Tong1
1Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, United States Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079 United States.
Chemical research in toxicology
|July 24, 2023
概括
使用变压器 (BERT) 双向编码器表示的人工智能 (AI) 可以自动将药物标签文档分类为标准化部分. 这种自然语言处理方法支持对药物安全性和有效性数据的有效监管审查.
科学领域:
- 监管科学 监管科学
- 医疗保健中的人工智能
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 美国食品和药物管理局 (FDA) 的监管提交要求有结构的文件,以有效分配审查员.
- 不一致的文档结构阻碍了整体数据审查,影响了药物安全性和有效性评估.
- 当前的做法往往导致信息被分散到各个部分,使综合分析变得复杂.
研究的目的:
- 为了评估基于人工智能的NLP方法,双向编码器从变压器 (BERT) 的表示,用于自动分类自由文本信息到标准化的监管部分.
- 通过改善文档组织,支持对药物安全性和有效性的更全面的审查.
- 评估模型在结构良好的和结构较差的FDA标签文件上的表现.
主要方法:
- 开发和训练基于BERT的NLP模型,以将文本分类为标准化部分.
- 使用FDA标签文件,特别是医生标签规则 (PLR) 结构,用于模型开发.
- 在各种数据集上评估模型,包括基于PLR,非PLR和产品特性摘要 (SmPC) 标签文件.
主要成果:
- 该模型在训练过程中获得了高准确度:96%的二进制分类和88%的多类分类.
- 测试准确度证明了强大的性能:二进制模型实现了95% (PLR),88% (非PLR),88% (SmPC);多类模型实现了82% (PLR),73% (非PLR),68% (SmPC).
- 人工智能语言模型有效处理未格式化的文档,将自由文本分类为标准化部分.
结论:
- 人工智能语言模型,特别是BERT,为处理非格式化文档提供了先进的监管科学方法.
- 将自由文本自动分类为标准化部分可以显著提高药物审查过程的效率和有效性.
- 这种方法有望改善监管机构的运作,并确保全面的药物安全性和有效性评估.
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