基于BERT的自然语言处理用于对药物不良反应报告的分类,其性能接近人类水平
Erik Bergman1, Luise Dürlich1,2,3, Veronica Arthurson1
1Swedish Medical Products Agency, Uppsala, Sweden.
PLOS digital health
|December 6, 2023
概括
本研究使用自然语言处理 (NLP) 来预测药物不良反应报告的严重性. 一个瑞典基于BERT的模型实现了接近人类水平的性能,通过优先考虑关键报告来提高患者安全.
科学领域:
- 药物监督 药物监督 药物监督
- 计算语言学 计算语言学
- 监管科学 监管科学
背景情况:
- 对药物不良反应 (ADRs) 的营销后监测对于药物安全至关重要.
- 高报告量挑战了监管机构的挑战,冒着严重副作用的延迟识别的风险.
- 准确及时评估ADR报告对于患者安全至关重要.
研究的目的:
- 开发和评估用于预测ADR报告严重性的自然语言处理 (NLP) 模型.
- 识别错误标记的报告,并优先考虑它们进行监管评估.
- 为了比较复杂的NLP模型与更简单的方法的性能.
主要方法:
- 利用了不同复杂度的四种不同的NLP模型.
- 采用引导重新抽样来进行可靠的性能估计,减轻随机效应.
- 进行前性测试以防止数据泄露并确保可概括性.
- 为分类任务微调瑞典基于BERT的语言模型.
主要成果:
- 瑞典基于BERT的语言模型在预测报告严重性方面表现接近人类水平.
- 更复杂的NLP模型,利用语言预训练,超过了比较简单的方法,如词袋和LSTM.
- 该研究成功地确定了NLP在优先评估ADR报告中的潜力.
结论:
- NLP,特别是基于变压器的模型,如BERT,可以有效地预测ADR报告的严重性.
- 语言预培训为准确的ADR报告分类提供了至关重要的稳定性.
- 这种方法可以增强药监系统,改善潜在安全问题的检测和管理.
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