使用自然语言处理对平民健康文章的自动分类:关于怀孕健康和产后抑郁症的案例研究
Braja Gopal Patra1, Zhaoyi Sun1, Zilin Cheng1
1Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.
Frontiers in psychiatry
|December 6, 2023
概括
利用自然语言处理 (NLP) 有助于对有关怀孕和心理健康的在线文章进行分类. 这种方法有效地引导个人获得可靠的健康信息,有助于预防产后抑郁症.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 公共卫生 公共卫生
背景情况:
- 通过优质的健康教育和沟通,可以加强产后抑郁症 (PPD) 的预防.
- 开发可靠的健康材料是资源密集的.
- 自然语言处理 (NLP) 为分类健康信息提供了一个潜在的解决方案.
研究的目的:
- 开发和评估NLP方法来分类与怀孕和心理健康相关的非专业文章.
- 确定有效的NLP模型来对与PPD风险因素相关的文章进行分类.
主要方法:
- 392篇在线专业文章的黄金标准集体被手动审查和分类.
- 文章根据与怀孕的相关性和子主题进行分类:饮食,运动,心理健康和健康素养.
- 包括随机森林,BERT和GPT-3.5-turbo在内的NLP模型被用于分类.
主要成果:
- 一个基于BERT的模型在端到端对相关性和主题的分类方面获得了F1分数0.974的F1分数.
- 在主题分类的两步方法中,GPT-3.5-turbo获得了0.972的F1得分.
- 这些模型在分类与PPD生活方式因素相关的文章方面表现出高准确度.
结论:
- NLP提供了一种可扩展和具有成本效益的方法,用于识别高质量的健康教育材料.
- 这种方法可以引导患者获取相关信息,提高健康素养,并可能预防产后抑郁症.
- 自动分类提高了卫生传播资源的可访问性和影响.
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