使用基于变压器的模型从非结构化乳腺成像报告中提取结构化数据
Mikel Carrilero-Mardones1, Jorge Pérez-Martín1, Francisco Javier Díez1
1Department of Artificial Intelligence, Universidad Nacional de Educacion a Distancia (UNED), Madrid, Spain.
Frontiers in digital health
|January 26, 2026
概括
像BioGPT这样的生成语言模型擅长将非结构化乳房成像报告转换为结构化数据. 这种自动化改进了临床数据策划和研究整合.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 临床数据通常是非结构化的自由文本,阻碍了研究和决策.
- 结构化的临床数据对于研究和知情决策至关重要.
- 这项研究解决了将非结构化乳腺成像报告转换为结构化数据的挑战.
研究的目的:
- 为了比较基于BERT和生成语言模型在构建乳腺成像报告中的性能.
- 评估用于将非结构化文本转换为表格数据的模型,用于临床和研究用途.
- 评估自然语言处理在医学数据提取中的有效性.
主要方法:
- 评估了五种基于变压器的模型 (BlueBERT,BioBERT,BioMedBERT,BioGPT,ClinicalT5) 在286个西班牙乳腺成像报告中.
- 用户对19个类别变量进行分类,并对4个实体进行提取性问题的回答.
- 测试了各种微调策略和输入配置,使用精度和宏 F1 评分进行评估.
主要成果:
- 在分类方面,BioGPT获得了最高的性能 (96.10%准确率,90.30%F1得分),超过了基于BERT的模型.
- 生物GPT在提取性问题回答方面表现强 (93.24%准确度),与其他顶级模型相比.
- 生物GPT提供了独特的同时分类和问答能力.
结论:
- 生成型模型,特别是BioGPT,为自动化从乳房成像报告中提取结构化信息提供了可扩展的解决方案.
- 由于BioGPT的卓越性能和多任务能力,可以显著减少手动数据整理工作.
- 这些发现支持将成像数据有效地整合到研究和临床工作流程中,使用先进的NLP.
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