在临床试验记录中数据共享声明的分类器
Saber Jelodari Mamaghani1, Cosima Strantz1, Dennis Toddenroth1
1Medical Informatics, University Erlangen-Nuremberg, Germany.
Studies in health technology and informatics
|August 23, 2024
概括
使用语言模型的分类器可以自动从临床试验数据共享声明 (DSS) 中识别可用的个人参与者数据 (IPD). 这些模型在预测手册标签时比原始类别表现更好,改善了IPD发现.
科学领域:
- 计算语言学计算语言学
- 临床试验数据管理
- 生物信息学是一种生物信息学.
背景情况:
- 来自临床试验的数字个人参与者数据 (IPD) 对科学再利用有价值.
- 识别可用的IPD需要在大型数据库中解释文本数据共享声明 (DSS).
- 计算语言学,特别是预训练的语言模型,为自动化这种解释提供了潜力.
研究的目的:
- 评估特定领域预训练语言模型在分类文本数据共享声明 (DSS) 的有效性.
- 为了比较分类器在复制原始可用性类别中的性能,与IPD手动注释标签相比.
- 评估这些分类器在帮助可用IPD的自动识别方面的潜力.
主要方法:
- 使用了来自clinicaltrials.gov.gov的5000个文本DSS的一个子集.
- 开发和评估基于特定领域预先训练的语言模型的分类器.
- 使用标准指标将分类器性能与原始可用性类别和手动注释的标签进行比较.
主要成果:
- 被训练来预测手动注释标签的分类人员的表现优于那些被训练在原始可用性类别上的分类人员.
- 这表明文本DSS包含了未被原始可用性类别所捕获的信息.
- 这些发现表明IPD自动识别的巨大潜力.
结论:
- 针对特定领域的预训练语言模型可以有效地分类文本数据共享声明.
- 手册注释为IPD可用性分类提供了比原始类别更丰富的信息.
- 这些先进的分类器可以显著帮助从大型临床试验数据库中自动发现可重复使用的个人参与者数据.
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