利用机器学习方法来预测潜在的莱姆病病例和美国的发病率,使用Twitter
Srikanth Boligarla1, Elda Kokoè Elolo Laison2, Jiaxin Li1
1Harvard Extension School, Harvard University, Cambridge, USA.
BMC medical informatics and decision making
|October 16, 2023
概括
这项研究使用了Twitter数据来追踪美国的莱姆病,发现推特和病例数量之间存在很强的相关性. 通过社交媒体监控,BERTweet证明有效地识别了潜在的莱姆病病例.
科学领域:
- 计算流行病学计算流行病学
- 公共卫生监督是对公共卫生的监督.
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 莱姆氏病是美国流行的载体传播疾病之一.
- 准确的发病率对于公共卫生管理至关重要.
研究的目的:
- 使用Twitter数据预测潜在的莱姆病病例.
- 评估美国莱姆病发病率.
- 评估社交媒体作为公共卫生监测工具.
主要方法:
- 收集并预处理了130万条推文,策划了77,500个标记数据集.
- 训练并测试NLP和机器学习模型 (TF-IDF,Word2vec,BERTweet) 用于推文分类.
- 分析了10年来与莱姆病相关的推特的时空模式.
主要成果:
- 在识别莱姆病推特时,BERTweet获得了最高的准确性和F1得分.
- 一个一致的模式显示,美国西部和东北部的推特率更高.
- 机密推特数量与官方莱姆病数量有很强的相关性.
结论:
- 推特数据可以成为美国莱姆病的宝贵监测工具.
- BERTweet是一个可靠的NLP分类器,用于检测相关的莱姆病推文.
- 社交媒体在提高莱姆病的认识方面发挥了作用.
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