自然语言处理模型在从自由文本放射学报告中提取信息的性能:系统性审查和元分析
Qingwen Yang1, Jiahui Jiang1, Xue Dong1
1Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong'an Road, Xicheng District, Beijing, 100050, China.
自然语言处理 (NLP) 模型在从非结构化的放射学报告中提取信息方面表现出高准确度. 需要进一步的研究来提高模型的通用性和报告标准化.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 放射学 放射学是一门学科.
背景情况:
- 自由文本放射学报告提供了灵活性,但导致数据未得到充分利用.
- 自然语言处理 (NLP) 可以自动提取信息,帮助结构化数据库开发.
研究的目的:
- 系统地审查和元分析NLP系统在从自由文本放射学报告中提取信息的性能.
主要方法:
- 在多个数据库中进行了系统的文献搜索.
- 通过QUADAS-2工具评估研究质量.
- 两变随机效应模型和元回归被用于分析.
主要成果:
- 分析了28项涉及51187份报告的研究.
- NLP系统实现了高的聚合灵敏度 (91%) 和特异性 (96%),AUC为0.98.
- 单个解剖部位的性能更好;解剖部位有助于异质性.
结论:
- NLP模型在从放射学报告中提取信息方面表现出色.
- 异质性表明需要加强报告标准化和模型通用性的需求.
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