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将自然语言处理应用于直肠癌MRI报告的后结构

W Liu¹, L Cai², Y Li³

¹Department of Radiology, Aerospace Center Hospital, Beijing, 100049, China; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.

Clinical radiology

|December 2, 2023

概括

一个自然语言处理 (NLP) 系统准确地从中国MRI报告中提取直肠癌成像特征. 与手动方法相比,这种NLP工具显著提高了数据提取效率.

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.
在瘤学瘤学.

背景情况:

结直肠癌的诊断依赖于磁共振成像 (MRI) 报告.
从自由文本MRI报告中提取结构化数据是一项挑战.
中国语言报告提出了独特的NLP挑战.

研究的目的:

开发和评估一种自然语言处理 (NLP) 系统,从中国直肠癌MRI报告中提取结构化数据.
评估NLP系统在识别关键成像特征方面的准确性和效率.
为了比较NLP的性能与放射科医生的手动提取.

主要方法:

基于规则的NLP模型是使用358个直肠癌MRI报告 (2015-2021) 开发的.
该模型在2015年之前的50份报告和2021年后的50份报告中进行了测试.
性能指标包括准确性,精度,回忆,F1得分和提取时间.

主要成果:

关键成像特征的报告率从36.55% (2015年之前) 增加到79.82% (2021年后).
NLP实现了高精度 (2015年之前93.82%,2021年后92.55%) 和F1得分 (2015年之前91.15%,2021年后96.29%).
在不到1秒的时间内,NLP提取了所有结构化信息.

结论:

基于规则的NLP系统能够从直肠癌MRI报告中快速准确地提取结构化数据.
结构化报告模板增强了基于NLP的信息提取.
在分析放射学报告方面,NLP显著提高了效率和准确性.

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