全球流行病或流行病和心理健康研究:自然语言处理研究
Xin Ye1,2, Xinfeng Wang3, Hugo Lin4
1Institute for Global Public Policy, Fudan University, 220 Handan Road, Yangpu District, Shanghai, 200433, China. yexin@fudan.edu.cn.
Journal of epidemiology and global health
|August 8, 2024
概括
自然语言处理 (NLP) 有效地综合了全球流行病和心理健康研究,确定了关键主题和研究差距. 这种方法对于基于证据的策略来缓解流行病期间的心理健康影响至关重要.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 心理健康研究 心理健康研究
背景情况:
- 全球流行病/流行病和心理健康研究的指数式增长超过了传统的系统审查能力.
- 需要先进的方法来综合大量关于公共卫生危机和心理健康的文献.
研究的目的:
- 使用自然语言处理 (NLP) 技术,系统地综合流行病/流行病和心理健康的证据.
- 绘制有关传染病爆发及其心理健康后果的研究格局.
主要方法:
- 在多个数据库中进行系统的文献搜索,截至2023年12月31日.
- 应用NLP技术,包括文本分类,主题建模和地理分析,用于数据分析.
- 根据内容,日期和地理位置对文章进行分类,以生成证据地图和趋势分析.
主要成果:
- 对77,915项研究的分析显示,COVID-19,SARS和HIV/AIDS是主要的流行病;焦虑和压力是常见的心理健康结果.
- 社交支持和获得医疗保健是经常被引用的应对机制.
- 研究主要来自高收入国家,低收入地区的数据有限;焦虑是全球普遍的主题,除了北美.
结论:
- NLP是一种可行的和有效的工具,用于对大规模的流行病和心理健康文献进行全面分析.
- 结果突出了未来研究的关键主题,并为基于证据的公共卫生战略提供信息,以解决流行病期间的心理健康问题.
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