关于COVID-19的流行病学调查的自动化分析框架
IEEE journal of biomedical and health informatics
|February 27, 2024
概括
本研究引入了一个自动化视觉流行病学调查分析 (AVESA) 框架,以简化COVID-19数据分析. 与手工方法相比,AVESA显著提高了效率和准确性,增强了流行病学见解.
科学领域:
- 流行病学 流行病学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 预防和控制COVID-19严重依赖于流行病学调查和病例报告分析.
- 目前对流行病学数据的手动分析是耗时和劳动密集的,阻碍了有效的疾病控制.
研究的目的:
- 为COVID-19设计和评估一个自动化的视觉流行病学调查分析 (AVESA) 框架.
- 提高分析流行病学调查报告的效率和准确性.
主要方法:
- 开发了一个深度神经网络,从COVID-19病例报告中提取信息.
- 使用预定义模式构建了一个流行病学知识图.
- 实现了用于图形分析的多维知识推理模型.
- 应用加权PageRank和Louvain算法进行风险和社区分析.
主要成果:
- 获得了85.12%的F1分数 (实体提取) 和92.29% (多任务提取),表现优于独立模型.
- 完整的图表计算结果与手动分析标准紧密结合.
- 权重的PageRank提高了Top_Recall_n%的11.21%;与手动分析相比,权重的卢瓦恩显示社区差异率为4.34%.
结论:
- AVESA框架为COVID-19流行病学分析提供了一种高效准确的自动化解决方案.
- 自动化知识图的构建和推理显著增强了流行病学调查分析.
- 与传统方法相比,AVESA在风险和社区检测方面表现优越.
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