开发基于规则的自然语言处理算法,以提取儿童初级保健患者的睡眠信息,以诊断睡眠
Joseph W Sirrianni1, Ariana Calloway1, Syed-Amad Hussain1,2
1Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States.
Sleep advances : a journal of the Sleep Research Society
|February 23, 2026
概括
一个新的,低资源的睡眠词汇有效地通过使用自然语言处理 (NLP) 在临床笔记中识别儿科睡眠问题. 这个工具有助于自动标记笔记以供进一步审查,改进睡眠健康分析.
科学领域:
- 计算语言学计算语言学
- 儿科健康信息学 儿科健康信息学
- 睡眠的药物 睡眠的药物
背景情况:
- 临床笔记包含有价值的多维和跨诊断信息.
- 从非结构化的儿科笔记中提取特定的健康信息,如睡眠问题,是具有挑战性的.
- 现有的方法可能缺乏在儿科睡眠健康中广泛应用的特异性或资源.
研究的目的:
- 开发和评估一种新的,低资源的睡眠词汇,用于在临床笔记中自动识别儿科睡眠相关的提及.
- 提高分析儿童临床文档睡眠健康问题的效率.
主要方法:
- 使用医学本体学,临床医生采访和临床笔记分析,创建了一个儿科睡眠词汇.
- 词汇包括技术术语,缩写和口语.
- 使用自然语言处理 (NLP) 将词汇应用于临床笔记.
主要成果:
- 该词汇在识别临床笔记与儿科睡眠相关提及时实现了高回忆率 (0.992) 和精度 (0.852).
- 假阳性主要与明确否认睡眠问题或讨论无关话题的笔记有关.
- 77.1%的与睡眠相关的文本包含至少一个词汇关键词.
结论:
- 开发的低资源睡眠词汇表现出了在标记层面上识别儿科睡眠提及的出色表现.
- 它提供了一个可部署的解决方案,用于临床笔记的初始选,并通过先进的模型或手动审查指导进一步的分析.
- 这种方法提高了从儿科临床记录中捕获关键睡眠健康信息的能力.
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