在临床自然语言处理中使用NegEx算法和卷积神经网络的组合进行否定识别
Guillermo Argüello-González1,2, José Aquino-Esperanza1,3, Daniel Salvador1
1MedSavana SL, Madrid, 28004, Spain.
BMC medical informatics and decision making
|October 13, 2023
概括
这项研究开发了一种新的方法来识别西班牙电子健康记录 (EHR) 中的否定,提高临床自然语言处理 (cNLP) 的准确性. 结合基于规则和神经网络的方法显著提高了现实世界的证据研究的可靠性.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 电子健康记录 (EHR) 在非结构化的自由文本中包含关键的患者信息.
- 在临床文本中识别否定修饰符是临床自然语言处理 (cNLP) 的重大挑战.
- 对于英语以外的语言,特别是西班牙语,有效的否定识别解决方案很少.
研究的目的:
- 为西班牙EHR开发一个强大的否定识别解决方案.
- 将定制的基于规则的NegEx层与卷积神经网络 (CNN) 结合起来,以提高准确性.
- 为了提高从EHR中提取临床命名实体 (cNE) 的可靠性.
主要方法:
- 采用二元分类方法 ("肯定的"与"非肯定的") 来识别否定.
- 一个基于规则的NegEx层是使用西班牙语的语料库规则和自定义添加程序来定制的.
- 一个CNN二进制分类器被训练在EHR上,医生对cNE和否定标记进行注释.
主要成果:
- 管道实现了高性能指标:0.93精度,0.94回忆,和0.94F1得分为"肯定的"类.
- 对于"非肯定的"类,管道获得了0.86精度,0.84回忆和0.85F1分数.
- 在单独的数据源上观察到一致的表现,在公共西班牙否定公司上获得了最先进的结果.
结论:
- 结合基于规则的NegEx层和CNN方法有效地解决了西班牙EHR中否定识别挑战.
- 这种方法显著提高了从临床自由文本中检索cNE的精度.
- 准确的否定识别对于减少假阳性和增加cNLP系统在现实世界证据研究中的可信度至关重要.
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