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识别COVID-19病例并从Reddit中提取患者报告的症状,使用自然语言处理

Muzhe Guo¹, Yong Ma², Efe Eworuke³

¹Department of Statistics, George Washington University, 2121 I St NW, Washington, DC, 20052, USA.

Scientific reports

|August 22, 2023

概括

自然语言处理 (NLP) 从社交媒体上准确识别了COVID-19病例和症状. 该方法追踪了早期,三角体和Omicron变种的症状变化,证明了用于公共卫生监测的高效数据提取.

科学领域:

计算流行病学计算流行病学
医疗信息学医学信息学
自然语言处理自然语言处理.

背景情况:

社交媒体平台为公共卫生监测提供了大量的实时数据.
追踪COVID-19病例和症状流行率对于了解疾病动态至关重要.

研究的目的:

开发和验证NLP模型,用于识别COVID-19病例并从社交媒体数据中提取症状.
分析不同COVID-19变种时期 (早期,三角形,欧米克朗) 症状流行率的变化.

主要方法:

利用了来自"covid19阳性"子reddit (03/2020-03/2022) 的社交媒体数据.
训练了一种来自变压器的双向编码器表示 (BERT) 分类模型用于案例识别 (91.2%的准确性).
开发了一种新的QuadArm模型 (问答,双体扩展,自适应旋转集群,映射) 用于症状提取.

主要成果:

在早期,三角形和奥米克朗时期分别确定了310,8794和12094名COVID阳性作者.
常见的早期症状包括咳,发烧,嗅觉丧失,头痛和喉疼痛.
报告的症状下降在三角形,减少嗅觉丧失和增加喉疼痛在奥米克朗.

结论:

NLP模型可以准确识别COVID-19病例,并有效地从社交媒体中提取症状数据.
这种方法为COVID-19变种不断演变的症状模式提供了宝贵的见解.
社交媒体分析是实时公共卫生监测的可行工具.

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