使用小波变换从时间序列数据中检测疫情爆发。
Detection of outbreaks from time series data using wavelet transform.
作者信息
Zhang Jun, Tsui Fu -Chiang, Wagner Michael M, Hogan William R
机构信息
Center of Biomedical Informatics, University of Pittsburgh, PA 15260, USA.
出版信息
AMIA Annu Symp Proc. 2003;2003:748-52.
In this paper, we developed a new approach to detection of disease outbreaks based on wavelet transform. It is capable of dealing with two problems found in real-world time series data, namely, negative singularity and long-term trends, which may degrade the performance of current approaches to outbreak detection. To test this approach, we introduced artificail disease outbreaks and negative singularities into a real world dataset and applied it and two other algorithms-autoregressive (AR) and Multi-resolution Wavelet Auto-regressive (MWAR) - to this dataset. We compared the performance of these algorithms in terms of sensitivity, specificity and timeliness. The results showed that our approach had similar sensitivity and specificity and slightly better timeliness compared to the other two algorithms. When we introduced negative singularities, its performance did not degrade as much as the other two algorithms' performance. We conclude that our approach to detection, when compared to traditional approaches, may not be as susceptible to degradation of performance caused by negative singularities.
在本文中，我们开发了一种基于小波变换的疾病爆发检测新方法。它能够处理实际时间序列数据中发现的两个问题，即负奇异性和长期趋势，这可能会降低当前爆发检测方法的性能。为了测试这种方法，我们将人工疾病爆发和负奇异性引入到一个真实世界的数据集，并将其与另外两种算法——自回归（AR）和多分辨率小波自回归（MWAR）——应用于该数据集。我们从灵敏度、特异性和及时性方面比较了这些算法的性能。结果表明，与其他两种算法相比，我们的方法具有相似的灵敏度和特异性，及时性略好。当我们引入负奇异性时，其性能下降的程度不如其他两种算法。我们得出结论，与传统方法相比，我们的检测方法可能不太容易受到负奇异性导致的性能下降的影响。
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