从结构薄弱的放射学报告中提取信息,使用自然语言查询
Amin Dada1, Tim Leon Ufer2, Moon Kim2
1Institute of AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany. amin.dada@uk-essen.de.
European radiology
|July 28, 2023
概括
我们开发了一个机器学习模型,使用BERT有效地从放射学报告中提取信息. 这种自然语言处理方法提高了准确性,节省了放射科医生的时间,最终有利于患者的护理.
科学领域:
- 医疗信息学医学信息学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 放射学报告包含关键的患者信息,但通常结构薄弱.
- 从这些报告中手动提取特定数据对医生和研究人员来说是耗时和低效的.
研究的目的:
- 开发和评估自然语言处理 (NLP) 机器学习模型,以有效地从放射学报告中提取信息.
- 为医生和研究人员提供一个自动化工具来查询放射学报告数据.
主要方法:
- 在857,783份未标记的放射学报告中评估了7个德国BERT模型.
- 使用1223份报告的注释阅读理解数据集 (SQuAD 2.0格式) 进行微调和评估.
- 基于性能指标进行比较的模型,包括F1得分和准确匹配精度.
主要成果:
- 对放射学数据和医学百科全书的BERT模型的持续预训练产生了最好的表现.
- 顶级模型在可回答问题的F1得分为83.97%,无法回答问题的准确率为96.01%.
- 该模型在问题措辞和处理域外问题的变化方面表现出稳定性.
结论:
- 在放射学数据上预先训练的BERT模型显著提高了放射学报告的问答准确性.
- 开发的NLP方法提供了一种有效的信息提取方法,减少了放射科医生的手工工作量.
- 将该工具集成到临床工作流程中可以释放放射科医生的资源,并间接改善患者护理.
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