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从法语的乳腺放射学报告中提取有效的临床信息

Jamil Zaghir^1,2, Belinda Lokaj^2,3, Karen Kinkel⁴

¹Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.

Studies in health technology and informatics

|August 23, 2024

概括

后勤回归有效地分类法国乳房MRI报告,用于自动数据提取. 这种方法通过提高确定关键患者信息的效率来增强临床支持和研究.

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:

放射学报告包含超越图像的重要患者数据.
自动提取这些信息对于临床支持和研究至关重要.
分类乳腺MRI报告有助于二次数据的利用.

研究的目的:

评估机器学习分类器,从法国乳腺MRI报告中自动提取数据.
为内部和外部验证确定表现最好的分类器.
评估从放射学报告中自动提取临床参数的可行性.

主要方法:

测试了来自两个瑞士临床中心的1,218份法国乳腺MRI报告的多个分类器.
使用后勤回归,比较其性能与其他模型.
通过使用内部和外部数据集的准确性和宏F1分数来评估分类器性能.

主要成果:

后勤回归在分类乳腺MRI报告方面表现出卓越的表现.
在内部数据上实现了高精度 (>0.95) 和宏F1 (>0.86).
在外部数据上显示出良好的准确性 (>0.81) 和宏F1 (>0.41),表明可概括性.

结论:

乳腺MRI报告的自动分类是可行的和有效的.
后勤回归是这项任务的合适方法,它促进了有效的临床参数提取.
这种自动化为未来的注释流程和研究提供了基础.

关键词:

乳腺癌乳腺癌乳腺癌在NLP中,我们使用了NLP.放射学报告放射学报告文字分类文本分类文本分类

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