新兴传染病监测使用分层诊断模型和诺克斯算法
Mengying Wang1,2, Bingqing Yang3, Yunpeng Liu3
1State Key Laboratory of Media Convergence and Communication, Communication University of China, No. 1, Dingfuzhuang East Street, Chaoyang District, Beijing, China.
Scientific reports
|November 14, 2023
概括
早期发现新出现的传染病至关重要. 一种新的双层模型,即新兴传染病检测模型 (EIDDM),能够准确地识别具有高特异性和积极预测价值的传染病,从而实现实时监测.
科学领域:
- 公共卫生 公共卫生
- 传染病流行病学 传染病流行病学
- 计算医学是一种计算医学.
背景情况:
- 新出现的传染病对全球健康和经济构成重大威胁.
- 早期检测和监测对于有效的公共卫生干预至关重要.
- 现有的监测系统可能缺乏针对新型病原体所需的速度和准确性.
研究的目的:
- 开发和评估一种用于早期检测新出现的传染病的新型计算模型.
- 评估模型在区分传染病和非传染病以及识别已知和未知的传染病原体方面的表现.
- 确定该模型是否适用于实时临床记录分析和疫情监测.
主要方法:
- 提出了一个两层的新兴传染病检测模型 (EIDDM),利用TextCNN-Attention进行二进制分类 (传染性与非传染性) 和使用LightGBM进行一对一休息策略进行多种分类 (已知传染性疾病).
- 该模型在来自北京五家医疗机构的37422个传染病和56133个非传染病记录的大数据集上进行了训练和验证.
- 使用准确度,灵敏度,特异性,正预测值和负预测值来评估性能,并与XGBoost和随机森林模型进行比较. 还使用诺克斯方法进行了时空分析.
主要成果:
- 第一层TextCNN-Attention模型在非传染性疾病中实现了高特异性 (97.57%) 和传染性疾病的积极预测值为95.07%.
- 第二层LightGBM模型显示,新兴传染病的平均预测准确率为90.44%.
- 对于单个在线推理,EIDDM的整体平均准确度为86.11%,快速响应时间约为27毫秒,并观察到传染病的时空集群 (P < 0.05).
结论:
- EIDDM是一个快速,准确和有效的工具,用于在现实世界的医院环境中早期检测和监测新出现的传染病.
- 该模型的高预测性能最大限度地降低了误诊风险,并支持及时的公共卫生反应.
- 时空分析揭示了聚类模式,突出了综合流行病学和计算方法对疾病监测的实用性.
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