Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada.
Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada.
J Biomed Inform. 2019 Jun;94:103181. doi: 10.1016/j.jbi.2019.103181. Epub 2019 Apr 20.
The algorithms used for detecting anomalies have evolved substantially over the last decade to take advantage of advances in informatics and to accommodate changes in surveillance data. We identified 145 studies since 2007 that evaluated statistical methods used to detect aberrations in public health surveillance data. For each study, we classified the analytic methods and reviewed the evaluation metrics. We also summarized the practical usage of the detection algorithms in public health surveillance systems worldwide. Traditional methods (e.g., control charts, linear regressions) were the focus of most evaluation studies and continue to be used commonly in practice. There was, however, an increase in the number of studies using forecasting methods and studies applying machine learning methods, hidden Markov models, and Bayesian framework to multivariate datasets. Evaluation studies demonstrated improved accuracy with more sophisticated methods, but these methods do not appear to be used widely in public health practice.
过去十年中，用于检测异常的算法已经有了很大的发展，以利用信息学的进步并适应监测数据的变化。自 2007 年以来，我们已经确定了 145 项评估用于检测公共卫生监测数据异常的统计方法的研究。对于每项研究，我们对分析方法进行了分类，并审查了评估指标。我们还总结了检测算法在全球公共卫生监测系统中的实际使用情况。传统方法（例如控制图、线性回归）是大多数评估研究的重点，并且在实践中仍然被广泛使用。然而，使用预测方法的研究以及应用机器学习方法、隐马尔可夫模型和贝叶斯框架对多变量数据集进行应用的研究数量有所增加。评估研究表明，更复杂的方法可以提高准确性，但这些方法似乎尚未在公共卫生实践中广泛使用。
