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首页> 外文期刊>BMC Medical Informatics and Decision Making >Recursive least squares background prediction of univariate syndromic surveillance data
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Recursive least squares background prediction of univariate syndromic surveillance data

机译：单变量综合监测数据的递归最小二乘背景预测

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Background Surveillance of univariate syndromic data as a means of potential indicator of developing public health conditions has been used extensively. This paper aims to improve the performance of detecting outbreaks by using a background forecasting algorithm based on the adaptive recursive least squares method combined with a novel treatment of the Day of the Week effect. Methods Previous work by the first author has suggested that univariate recursive least squares analysis of syndromic data can be used to characterize the background upon which a prediction and detection component of a biosurvellance system may be built. An adaptive implementation is used to deal with data non-stationarity. In this paper we develop and implement the RLS method for background estimation of univariate data. The distinctly dissimilar distribution of data for different days of the week, however, can affect filter implementations adversely, and so a novel procedure based on linear transformations of the sorted values of the daily counts is introduced. Seven-days ahead daily predicted counts are used as background estimates. A signal injection procedure is used to examine the integrated algorithm's ability to detect synthetic anomalies in real syndromic time series. We compare the method to a baseline CDC forecasting algorithm known as the W2 method. Results We present detection results in the form of Receiver Operating Characteristic curve values for four different injected signal to noise ratios using 16 sets of syndromic data. We find improvements in the false alarm probabilities when compared to the baseline W2 background forecasts. Conclusion The current paper introduces a prediction approach for city-level biosurveillance data streams such as time series of outpatient clinic visits and sales of over-the-counter remedies. This approach uses RLS filters modified by a correction for the weekly patterns often seen in these data series, and a threshold detection algorithm from the residuals of the RLS forecasts. We compare the detection performance of this algorithm to the W2 method recently implemented at CDC. The modified RLS method gives consistently better sensitivity at multiple background alert rates, and we recommend that it should be considered for routine application in bio-surveillance systems.
机译：背景技术单变量综合征数据的监测已被广泛用作发展公共卫生状况的潜在指标。本文旨在通过使用基于自适应递归最小二乘法的背景预测算法，并结合新颖的“星期几”效应处理，来提高暴发检测的性能。方法第一作者的先前工作表明，对综合征数据进行单变量递归最小二乘分析可用于表征构建生物监测系统的预测和检测组件的背景。自适应实现用于处理数据的非平稳性。在本文中，我们开发并实现了用于单变量数据背景估计的RLS方法。然而，一周中不同日期的数据分布明显不同，这可能会对过滤器的实现产生不利影响，因此，引入了一种基于日计数排序值的线性变换的新颖过程。每日预测计数提前7天用作背景估计。信号注入过程用于检查集成算法在实际症状时间序列中检测合成异常的能力。我们将该方法与称为W2方法的基线CDC预测算法进行了比较。结果我们使用16组综合数据，以四种不同注入信噪比的接收器工作特性曲线值的形式呈现检测结果。与基线W2背景预测相比，我们发现虚警概率有所改善。结论结论本文介绍了一种针对城市级生物监测数据流的预测方法，例如门诊就诊时间序列和非处方药的销售。这种方法使用RLS过滤器，该过滤器通过对这些数据系列中经常出现的每周模式进行校正而进行了修改，并使用了基于RLS预测残差的阈值检测算法。我们将该算法的检测性能与CDC最近实施的W2方法进行了比较。改进的RLS方法在多个背景警报率下始终具有更好的灵敏度，我们建议在生物监视系统中常规应用时应考虑使用它。

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