预测科学文章对新出现的流行病的有用性,使用其标题和自然语言处理
Kinga Dobolyi1, Sidra Hussain1, Grady McPeak1
1Department of Computer Science, George Washington University, Washington, DC, USA.
Disaster medicine and public health preparedness
|May 10, 2024
概括
深度学习模型可以通过单独分析标题来自动评估科学文章对公共卫生政策的有用性. 这有助于在COVID-19等新型疾病爆发期间过关键信息.
科学领域:
- 公共卫生 公共卫生
- 计算语言学 计算语言学
- 流行病学 流行病学
背景情况:
- 疫情期间的科学出版物往往缺乏政策制定者可行的见解.
- 随着COVID-19研究的数量增加,在疫情早期识别相关证据方面面临挑战.
- 评估科学文章对政策的有用性对于疾病缓解至关重要.
研究的目的:
- 开发一种自动化方法来判断科学文章对公共卫生政策的有用性.
- 评估仅使用文章标题进行评估的可行性.
- 帮助决策者在新型疾病爆发期间浏览科学文献.
主要方法:
- 在CORD-19数据集中的COVID-19出版物标题上训练深度学习自然语言处理 (NLP) 模型.
- 利用专家策划的COVID-19证据进行模型评估.
- 专注于基于标题的分析,用于自动选.
主要成果:
- 证明深度NLP模型可以根据标题单独准确地判断COVID-19文章的实用性.
- 确认了自动查对公共卫生政策相关性的可行性.
- 展示了NLP在健康危机期间评估科学文献的潜力.
结论:
- 对于公共卫生专家来说,NLP模型是有效的工具,可以对新型疾病的日常出版物进行分类和过.
- 自动化标题分析可以显著提高基于证据的政策制定效率.
- 这种方法对于在COVID-19等大流行期间管理信息过载是有价值的.
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