一种自然语言处理方法,以协调沟通在欧洲药物授权过程中发现的不确定性
Stefan Verweij1,2, Vincent Haverhoek1,3, Erik Bergman4
1Dutch Medicines Evaluation Board, Utrecht, The Netherlands.
Clinical pharmacology and therapeutics
|February 12, 2024
概括
这项研究使用自然语言处理将描述欧洲公共评估报告中医学不确定性的句子组合起来. 这种方法旨在协调如何向公众传达风险和不确定性.
科学领域:
- 药物监督 药物监督 药物监督
- 监管科学 监管科学
- 自然语言处理自然语言处理.
背景情况:
- 欧洲公共评估报告 (EPAR) 提供了关于药物风险和不确定性的关键信息.
- 在EPAR不确定性描述的变化阻碍了医疗保健专业人员和患者的清晰沟通.
- 协调EPAR中使用的语言对于对药物安全性的一致理解至关重要.
研究的目的:
- 开发和应用一种自然语言处理 (NLP) 方法,用于从EPAR的不确定性部分中分组句子.
- 为了确定语义上的相似性和有关医学不确定性的群体相关陈述.
- 作为协调监管文件中不确定性的沟通的基础.
主要方法:
- 使用BERT语言模型进行句子嵌入和语义分析.
- 应用统一多重近似和投影 (UMAP) 用于缩小维度.
- 应用程序与噪音的基于员工密度的空间聚类 (DBSCAN) 来识别句子集群.
- 根据总体主题标记集群,并以药物属性 (例如,疗效,副作用) 进行表征.
主要成果:
- 分析了553个EPAR的不确定性部分中的13,105个句子.
- 识别了26个不同的语义上相似的句子集群,其中2个集群被指定为噪音.
- 聚类揭示了共同的主题和不同药物的不确定性描述方式的变化.
结论:
- NLP方法有效地将与EPAR中的药物不确定性相关的句子聚集在一起.
- 已识别的集群为描述风险和不确定性的语言标准化提供了基础.
- 这项工作支持监管机构和开发人员为公众制定关于药物安全的统一沟通策略.
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