针对COVID-19和健康的人力资源新闻信息提取:算法开发和验证
Mathieu Ravaut1, Ruochen Zhao1, Duy Phung1
1Nanyang Technological University, Singapore, Singapore.
JMIR AI
|October 30, 2024
概括
这项研究表明,将自然语言处理 (NLP) 与人类分析相结合,有效地处理了关于COVID-19大流行及其卫生工作人员影响的大量新闻数据,从而为政策提供了及时的见解.
科学领域:
- 计算语言学计算语言学
- 公共卫生信息学 公共卫生信息学
- 医疗服务研究 医疗服务研究
背景情况:
- 像COVID-19这样的全球流行病产生了大量的在线新闻.
- 分析这些信息对于理解事件和指导政策至关重要.
- 人类能力不足以有效处理这种数据洪水.
研究的目的:
- 探索自然语言处理 (NLP) 以快速分析大量新闻.
- 开发一个人机共生工作流程,以获得卫生工作人员的洞察力.
- 支持战略政策对话,倡导和决策.
主要方法:
- 从WHO EIOS查看了280万篇COVID-19卫生工作人员新闻文章 (2020年1月至2022年6月).
- 使用NLP模型 (分类,提取总结) 和人类分析.
- 开发了DeepCovid系统,在各种全球来源上进行了培训.
主要成果:
- 基于规则的分类将精细数据分为8508个相关文章.
- 在DeepCovid的分类中,它实现了98.98%的ROC-AUC.
- 提取性总结实现了平均ROUGE得分为47.76.76.
结论:
- 将NLP模型和人体分析协同使用,对于健康工作人员的智能化是可行的.
- DeepCovid的方法提供了一个敏捷的,及时的全球视角.
- 这个系统用开源智能来补充科学文献.
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