使用自然语言处理来识别临床文本中的性和生殖健康信息
Elizabeth I Harrison1, Laura A Kirkpatrick1, Patrick W Harrison2
1Department of Pediatrics, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, United States.
这项研究开发了一种可复制的方法,用于使用自然语言处理 (NLP) 在临床笔记中分析敏感的健康主题. 该方法准确地提取性和生殖健康信息,与专家审查相似.
科学领域:
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
- 公共卫生 公共卫生
背景情况:
- 从临床笔记中提取敏感的健康信息对于没有专业专业知识的研究人员来说是一项挑战.
- 大量的电子健康记录 (EHR) 包含有关性和生殖健康 (SRH) 的宝贵数据.
研究的目的:
- 开发和验证一种可复制的方法,使临床研究人员能够从大量临床笔记数据集中提取和分析SRH信息.
- 通过代机器学习模型应用,实现与SRH相关的新相关词汇的识别.
主要方法:
- 使用scispaCy进行句子细分和spaCy用于机器学习文本分类 (字袋和带注意力的神经网络).
- 采用正则表达式和手动注释的组合进行初始数据标签,然后进行代模型训练和应用.
- 通过使用Cohen's kappa验证了对主题专家手册审查的方法,以确保评审者之间的可靠性和比较.
主要成果:
- 该方法应用于971名女性患者的3,663个儿童神经病学笔记,重点关注六个SRH类别.
- 在主题专家之间 (科恩的卡帕从0.8864到1) 和在方法和专家审查之间 (科恩的卡帕从0.9841到1) 达成了高度一致.
结论:
- 开发的方法是可重复的,并且有效地分析从临床笔记中大量的文本.
- 基于NLP的方法产生了与主题专家手动审查非常相似的结果,促进了敏感健康数据分析.
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