在心理健康记录中识别疼痛的提及 文本:一种自然语言处理方法
Jaya Chaturvedi1, Sumithra Velupillai1, Robert Stewart1,2,3
1Institute of Psychiatry, Psychology and Neurosciences, King's College London.
Studies in health technology and informatics
|January 25, 2024
概括
这项研究开发了一种机器学习模型,用于识别心理健康电子健康记录中的患者疼痛提及. 该模型准确地提取疼痛信息,改进了疼痛和心理健康联系的研究.
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.
- 心理健康研究 心理健康研究
背景情况:
- 疼痛是一个常见的医疗保健问题,研究对其与心理健康的交叉越来越感兴趣.
- 心理健康电子健康记录 (EHR) 提供了有价值的数据,但主要以非结构化的自由文本形式包含疼痛信息.
- 从临床笔记中提取疼痛细节是具有挑战性的,因为疼痛语言的模糊性质.
研究的目的:
- 开发和评估一种机器学习分类算法,用于在心理健康电子健康记录中识别讨论患者疼痛的句子.
- 促进从大型电子健康记录数据库中自动提取关键的疼痛相关信息.
主要方法:
- 利用了一个匿名的心理健康电子记录数据库.
- 手动三重注释1985份文件,以创建一个黄金标准数据集.
- 训练并比较了四种不同的机器学习分类算法.
主要成果:
- 性能最好的分类模型获得了0.98的高F1得分 (95%信心区间:0.98-0.99).
- 这表明他能够非常准确地区分讨论患者疼痛的句子.
结论:
- 基于机器学习的文本分类对于从心理健康电子健康记录中提取疼痛信息是有效的.
- 这种方法可以显著帮助研究疼痛和心理健康之间的复杂关系.
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