对主题挖掘和主题预测的新方法的研究,用于新出现的传染病的医学预印件
Zongjing Liang1, Yun Kuang2, Gongcheng Liang3
1School of Economics and Management, Guangxi Normal University, Guilin, CHN.
Cureus
|July 14, 2025
概括
这项研究引入了一种新的预测框架,结合了公众关注和预印主题分析,以实时监测传染病研究趋势. 该方法使用谷歌趋势和隐性迪里克莱特分配 (LDA) 与自行回归分布式滞后 (ARDL) 模型进行及时的洞察.
科学领域:
- 公共卫生信息学 公共卫生信息学
- 计算语言学 计算语言学
- 流行病学 流行病学
背景情况:
- 传染病的持续风险需要实时监测研究趋势.
- 传统的主题预测方法存在滞后,阻碍及时响应.
- 将公众关注指标与医疗预印花分析相结合,提供了一个解决方案.
研究的目的:
- 提出一种新的预测框架,用于实时监测传染病研究.
- 通过整合公众关注数据来提高主题预测的及时性.
- 分析研究主题强度与公众关注之间的动态关系.
主要方法:
- 使用网络爬虫从medRxiv收集了18,060个COVID-19预印件.
- 应用隐藏的迪里克莱特分配 (LDA) 用于未经监督的主题建模预印摘要.
- 利用自行回归分布式滞后 (ARDL) 模型来分析公众注意力和话题强度之间的动态关系.
- 执行文本预处理,时间序列聚合,静态性测试 (ADF) 和差异化.
主要成果:
- 通过LDA通过COVID-19预版中确定了七个主要研究主题.
- ARDL的分析证实了公共搜索趋势与研究主题强度之间的显著动态关系.
- 开发的模型表现出良好的预测性能.
结论:
- 结合了LDA和ARDL模型,创建了一个实时预测方法,用于跟踪医学预印题目演变.
- 该框架为公共卫生信息学提供了重要的理论和实践价值.
- 为监测和预防未来传染病爆发提供了一种可行的方法.
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