用于医学研究文件的手写表格的转录
Luca Kohlhepp1, Sabine Busies2, Ute Zirrgiebel2
1CAIDAS (Center of AI and Data Science), Univ. Würzburg, Germany.
Studies in health technology and informatics
|September 3, 2025
概括
使用光学文件识别和大型语言模型 (LLM) 的临床试验主文件 (eTMF) 的自动转录大大缩短了数据输入时间. 与手动转录方法相比,这种技术可以大大节省时间.
科学领域:
- 临床数据管理
- 医疗保健中的人工智能
背景情况:
- 在电子临床试验总文件 (eTMF) 中手动输入数据是一个费力和耗时的过程.
- 有效管理临床试验文档对于合规性和运营效率至关重要.
研究的目的:
- 评估用于填充eTMF数据库的自动转录方法的有效性.
- 将光学文件识别 (ODR) 和多式大型语言模型 (LLM) 的性能进行比较,用于文档分析.
主要方法:
- 开发和应用一个光学文件识别 (ODR) 管道.
- 实施和测试基于网络 (全球) 和本地多式联络大型语言模型 (LLM).
- 在不同类型的文档中对转录准确性和效率进行实验验证.
主要成果:
- 这项研究表明,在基于表的文档中,不同的自动化方法是最佳的.
- 光学文件识别和LLM在准确转录临床试验文件中的信息方面表现有前途.
- 根据具体的文档结构和内容,表现有所不同.
结论:
- 与传统的手动数据输入相比,使用ODR和LLM的自动转录可以大大节省时间.
- 这些发现表明了简化eTMF数据管理的可行技术解决方案.
- 进一步优化LLM和ODR方法可以提高临床试验操作的效率.
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