在美国部署的军事力量中调查失眠:一个主题建模方法
Edwin W D'Souza1, Andrew J MacGregor2, Rachel R Markwald3
1Leidos, Inc., San Diego, California, USA; Medical Modeling, Simulation, and Mission Support Department, Naval Health Research Center, San Diego, California, USA.
Sleep health
|December 9, 2023
概括
临床笔记的主题建模确定了军事人员的关键失眠主题,包括压力,焦虑和疼痛,为部署相关的睡眠问题提供了洞察力.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 失眠是一个普遍的问题,特别是在部署的军事人员中.
- 了解失眠的根本原因对于有效治疗至关重要.
- 电子健康记录包含丰富,非结构化的数据,可以用于研究.
研究的目的:
- 将主题建模应用于被诊断患有失眠的部署美国军事人员的自由文本临床笔记.
- 在这个人群中确定与失眠相关的主题模式和潜在病因.
- 探索主题建模在分析睡眠研究电子健康记录数据中的实用性.
主要方法:
- 美国国防部军事卫生系统对32,864名独特患者的临床记录进行了回顾性分析.
- 应用主题建模,一种自然语言处理技术,以识别潜在的主题.
- 验证机器学习主题与人类编码的潜在失眠病因问题的验证.
主要成果:
- 选择了12个主题模型,包括个人病史,兴奋剂,压力,家庭问题,其他睡眠障碍,抑郁症,日程安排,焦虑,其他药物,头痛/脑震荡,疼痛和药物补充等主题.
- 在验证的主题 (不包括与药物相关的主题) 和人类编码的病因因素之间发现了强烈的一致性.
- 确定的主题在很大程度上与已知的失眠相关因素保持一致.
结论:
- 主题建模有效地识别了与部署军事人员失眠相关的临床笔记中的主题模式.
- 该研究强调了部署相关失眠的多种潜在病因,包括压力,焦虑和疼痛.
- 主题建模可以增强电子健康记录数据,为临床医生,研究人员和医疗保健系统提供宝贵的见解.
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