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在初级保健中识别枪支暴力暴露 临床笔记:制定国家语言处理文本分类器的协议

Natalie Carwright1, Frances M Biel2, Megan Hoopes2

  • 1Department of Mathematics, Norwich University, Northfield, VT, United States.

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这项研究开发了一种新的自然语言处理 (NLP) 文本分类器,用于识别电子健康记录中的枪支暴力暴露. 通过更好地了解枪支暴力对健康的影响,

关键词:
枪支伤害枪支暴力自然语言处理,电子健康记录文字分类器

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科学领域:

  • 公共卫生
  • 医疗信息学
  • 计算语言学

背景情况:

  • 结构化的电子健康记录 (EHR) 数据不足以捕捉枪支暴力的全部范围,包括次要经历.
  • 第二次接触,比如目睹暴力或失去亲人,
  • 电子健康记录中的临床注释包含丰富的非结构化数据,可用于识别这些暴露.

研究的目的:

  • 开发一种自然语言处理 (NLP) 文本分类器,以识别主要和次要的枪支暴力.
  • 分析5岁及以上的门诊初级保健和行为健康临床记录中的暴露数据.
  • 加强在临床环境中对枪支暴力的调查.

主要方法:

  • 在2012年至2022年间使用OCHIN,一个多州EHR网络的非结构化临床记录.
  • 通过词典识别和手动文本审查开发一个标记的数据集,用于监督NLP.
  • 构建,培训和评估机器学习,神经网络和用于文本分类的大型语言模型.
  • 让利益相关者咨询委员会确保方法严格,并解决潜在的偏见.

主要成果:

  • 该研究目前处于NLP文本分类器的评估阶段,预计到2025年8月将最终选择模型.
  • 预计NLP模型的开发和性能结果将在2026年公布.
  • 持续开发和评估用于识别枪支暴力的NLP模型.

结论:

  • 这项研究引入了一种新的NLP文本分类器,用于在临床笔记中识别枪支暴力.
  • 开发的NLP模型有可能增加暴露患者的识别.
  • 这项工作为了解枪支暴力对健康的长期影响和改善患者护理策略奠定了基础.