Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723, USA.
Stat Med. 2011 Jun 30;30(14):1665-77. doi: 10.1002/sim.4204. Epub 2011 Mar 22.
Algorithms for identifying public health threats or disease outbreaks are vulnerable to false alarms arising from sudden shifts in health-care utilization or data participation. This paper describes a method of reducing false alerts in automated public health surveillance algorithms, and in particular, automated syndromic surveillance algorithms, that rely on health-care utilization data. The technique is based on monitoring syndromic counts with reference to a suitable background, or reference, series of counts. The suitability of the background time series in decreasing the false-alarm rate will be shown to be related mathematically to the so-called mutual information that exists between the random variables representing the syndromic and background time series of counts. The method can be understood as a noise cancellation filter technique in which one noisy (reference) channel is used to cancel the background noise of the monitored (measured) channel. The issues discussed here may also be relevant to the appropriate use of rates in epidemiology and biostatistics.
用于识别公共卫生威胁或疾病暴发的算法容易受到医疗保健利用率或数据参与的突然变化引起的误报的影响。本文描述了一种减少依赖医疗保健利用率数据的自动公共卫生监测算法，特别是自动综合征监测算法中的误报的方法。该技术基于使用合适的背景或参考计数系列来监测综合征计数。将显示背景时间序列在降低误报率方面的适用性与代表综合征和背景计数时间序列的随机变量之间存在的所谓互信息在数学上相关。该方法可以理解为噪声消除滤波器技术，其中使用一个嘈杂（参考）通道来消除监测（测量）通道的背景噪声。这里讨论的问题也可能与流行病学和生物统计学中比率的适当使用有关。
