根据Zhihu评论数据识别过敏性鼻炎的危险因素,使用主题增强的词嵌入模型:混合方法研究和集群分析
Dongxiao Gu1, Qin Wang1, Yidong Chai1
1School of Management, Hefei University of Technology, Hefei, China.
Journal of medical Internet research
|February 22, 2024
概括
社交媒体评论揭示了过敏性鼻炎 (AR) 的主要风险因素,如季节,地区和虫. 这种分析有助于理解触发因素,并制定AR的管理策略.
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
- 过敏研究研究 过敏研究
背景情况:
- 过敏性鼻炎 (AR) 是一种慢性疾病,受到日常环境暴露的影响.
- 社交媒体平台有助于广泛分享个人健康经验和信息.
研究的目的:
- 开发一种智能方法 (TopicS-ClusterREV) 来识别来自社交媒体评论的AR风险因素.
- 将已识别的风险因素分类,并了解它们触发AR的机制.
主要方法:
- 搜索了9628个帖子和33,747条评论,这些帖子与来自齐胡的过敏性鼻炎有关 (2012年5月至2022年5月).
- 利用一个改进的Skip-gram模型用于主题增强的词向量表示 (TopicS).
- 使用风险因素分类器和集群分析来分类和分析触发因素.
主要成果:
- 话题S-ClusterREV分类器在识别AR风险因素方面实现了96.1%的准确性和96.3%的回忆.
- 识别和分类了28个不同的风险因素,其中季节,地区和虫是最普遍的.
- 提供了有关机制的见解,例如季节性变化扰乱免疫系统.
结论:
- 开发的方法有效地从大型社交媒体数据集中提取AR风险因素.
- 已识别的风险因素及其触发因素为个人提供了切实可行的指导,以减少AR.
- 结果可以为制定有针对性的AR管理和干预策略提供信息.
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