基于本体学的数据收集用于使用社交媒体的混合爆发检测方法
IEEE transactions on nanobioscience
|August 13, 2024
概括
本研究介绍了一种混合XGBoost和双向长短期记忆 (BiLSTM) 模型,用于使用Twitter数据预测传染病. 该模型增强了疫情跟踪和预测准确性,用于公共卫生.
科学领域:
- 流行病学 流行病学
- 计算健康 计算健康
- 数据科学数据科学数据科学
背景情况:
- 快速传播的疾病构成了全球性挑战,COVID-19大流行凸显了这一点.
- 在线社交媒体平台提供及时的公共卫生信息传播.
- 传统的疾病检测方法可以通过实时数据分析来增强.
研究的目的:
- 利用社交媒体数据开发一种有效的传染病预测模型.
- 利用推特数据和本体学来识别和策划相关的疾病症状推特.
- 改善流行病学见解和疫情追踪能力.
主要方法:
- 开发了一种混合模型,集成XGBoost和双向长短期内存 (BiLSTM) 架构.
- XGBoost用于处理小型数据集,并从多变量时间序列数据中识别最佳特征.
- 实体学被用来从与传染病症状相关的Twitter数据中策划相关的推文.
主要成果:
- 混合XGBoost-BiLSTM模型与最先进和基线模型相比,显示出优异的预测性能.
- 对多个传染病爆发的数据集进行了广泛的实验,验证了该模型的有效性.
- 该模型显示了更高的预测准确度和更好的疫情追踪能力.
结论:
- 拟议的混合模型为实时传染病预测提供了一个有希望的方法.
- 这一框架可以帮助卫生当局减轻死亡人数,并为未来的疫情做好准备.
- 利用社交媒体数据与先进的机器学习来增强公共卫生监测.
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