自然语言处理将非结构化的COVID-19胸部CT报告转换为结构化的报告
Salvatore Claudio Fanni1, Chiara Romei2, Giovanni Ferrando3
1Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy.
European journal of radiology open
|August 14, 2023
概括
深度学习模型自动将非结构化的COVID-19CT报告转换为结构化数据,提高准确性并实现数据挖掘. 这克服了放射科医生传统结构化报告的局限性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 结构化报告提高了完整性,减少了放射学报告中的错误,促进了数据挖掘.
- 放射科医生认为结构化报告具有限制性,限制言论自由.
研究的目的:
- 开发基于深度学习的自然语言处理方法,将非结构化的COVID-19胸部CT报告自动转换为结构化报告.
- 克服传统结构化报告的局限性.
主要方法:
- 一个卷积神经网络被训练在202个COVID-19胸部CT报告上,使用放射科医生生成的结构化报告作为基础真相.
- 该模型提取了62个分类变量,使用平均准确度和F1得分在两个代 (有和没有微调) 中评估性能.
- 错误分析确定了不正确模型处理的来源.
主要成果:
- 该模型在第一个代中实现了93.7%的平均准确率和93.8%的F1得分,其中46%的错误是由于错误的推断.
- 经过微调后,两项指标的性能均提高到95.8%,推断误差降至26%.
结论:
- 一个卷积神经网络有效地自动化了自由形式放射文本的转换成结构化报告.
- 这种方法克服了结构化报告的局限性,并促进了放射性数据挖掘.
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