病诊所信件的注释用于自然语言处理
Beata Fonferko-Shadrach1, Huw Strafford2, Carys Jones2
1Swansea University Medical School, Swansea University, Swansea, Wales, UK. b.fonferko-shadrach@swansea.ac.uk.
Journal of biomedical semantics
|September 14, 2024
概括
研究人员创建了合成临床文档,以训练和验证自然语言处理 (NLP) 管道. 自动提取临床文本版本2 (ExECTv2) 管道在从非结构化文本中提取信息方面表现出高准确性.
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.
- 临床研究 临床研究
背景情况:
- 自然语言处理 (NLP) 对于从临床文本中提取结构化数据至关重要,以支持决策和研究.
- 缺乏专家注释的临床文件,阻碍了NLP工具的开发和验证.
- 合成数据生成提供了一个解决方案来增强有限的注释数据集.
研究的目的:
- 为培训和验证NLP管道创建合成临床文档.
- 开发和评估临床文本提取版本2 (ExECTv2) NLP管道的性能.
- 提供公开可用的注释性诊所信件资源.
主要方法:
- 创建了200个合成诊所信件,模拟了的门诊咨询.
- 由经过培训的临床医生和研究人员使用标记工具和概念列表,采用双重注释.
- 通过注释审查和共识建立了一个黄金标准数据集,以验证ExECTv2管道.
主要成果:
- 在人类注释方面获得了0.73的IAA F1评分.
- 在ExECTv2管道中,每项F1得分为0.87,每字母F1得分为0.90.
- 与人类注释者相比,自动化NLP管道的表现优越.
结论:
- 自由发布的合成字母,注释和NLP研究的指导方针.
- 突出了临床文本注释的挑战和黄金标准的必要性.
- 证实了ExECTv2 NLP管道的实用性和准确性,用于从非结构化的临床文本中提取详细的信息.
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