军队卫生资料中季节性流感高峰的预测

IF 2.3 Q3 ENGINEERING, BIOMEDICAL Biomedical Engineering and Computational Biology Pub Date : 2016-04-19 eCollection Date: 2016-01-01 DOI:10.4137/BECB.S36277
Anna L Buczak, Benjamin Baugher, Erhan Guven, Linda Moniz, Steven M Babin, Jean-Paul Chretien
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引用次数: 5

摘要

流感是一种高度传染性疾病,可引起季节性流行病,发病率和死亡率很高。提前数周预测流感高峰的能力将允许及时进行预防性公共卫生规划和干预措施,以减轻这些疫情。由于流感也可能影响现役人员的作战准备,美国军方高度重视季节性疫情的监测和准备工作。利用先进的数据挖掘技术,利用不同的流行病学和环境数据,开发了一种方法,可以创建模型,提前数周预测流感高峰就诊次数(即活动)。给出了模型结果,并与其他流行的数据挖掘分类器进行了比较。通过对模型在开发过程中未使用的数据进行严格测试,结果表明,该技术可以预测特定地区流感活动最高的一周,总体上比本文所研究的其他方法更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Prediction of Peaks of Seasonal Influenza in Military Health-Care Data.

Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article.

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