雷德克斯 (RADEX):一种基于规则的临床和放射学数据提取工具,在甲状腺超声波报告中证明了这一点
Lewis Howell1,2, Amir Zarei3, Tze Min Wah3,4
1School of Computing, University of Leeds, Leeds, LS2 9JT, UK. scljh@leeds.ac.uk.
European radiology
|February 13, 2025
概括
一个新的基于规则的工具,RADEX,自动地从放射学报告中提取信息. 它有效地分类甲状腺超声波发现,为研究和审计提供节省时间的解决方案.
科学领域:
- 医疗信息学 医疗信息学
- 放射学 放射学是一门学科.
- 自然语言处理自然语言处理.
背景情况:
- 放射学报告包含有价值的研究数据,但它往往被锁定在非结构化的自由文本中.
- 手动提取这些信息耗时且具有挑战性,阻碍了二次分析和人工智能模型培训.
研究的目的:
- 开发和评估一个基于规则的工具,RADEX (放射学数据提取),用于从临床文件中自动提取信息.
- 为了简化将领域专业知识转化为可搜索模型的过程,而不需要专业的自然语言处理 (NLP) 知识.
主要方法:
- 雷德克斯是使用基于规则的方法开发的,将域专业知识转换为正则表达式模型.
- 它的实用性在一个大型数据集上进行了测试,包括16246份英国甲状腺超声波报告,用于对14种临床特征进行多标签分类.
- 性能使用对照标准标签的持久性测试来评估.
主要成果:
- 雷德克斯在分类甲状腺超声波发现方面取得了高性能,微平均灵敏度为0.97,特异性为0.96,F1得分为0.94.
- 该工具以每条报告12.3毫秒的速度进行了快速处理.
- 参考标签的评价者之间的可靠性很高 (科恩的卡帕0.94-0.95).
结论:
- 雷德克斯 (RADEX) 是一种多功能,节省时间,免费可用的工具,用于从放射学报告中提取结构化数据.
- 它对于具有有限标记数据或计算资源的研究和审计应用特别有用,优先考虑可解释性和可重复性.
- 该工具加速了数据收集,提供了新的见解,并可能改善患者护理.
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