Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction
Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction
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DOI：
10.1016/j.artmed.2020.101818
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发表时间：
2020-04-01
影响因子：
7.5
通讯作者：
Lucia Lopez, M. D.
中科院分区：
文献类型：
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作者：
Jimenez, Fernando;Palma, Jose;Lucia Lopez, M. D.
抗菌素耐药性已成为最重要的健康问题之一，全球已提出了全球行动计划。预防在这些行动计划中起着关键作用，在这方面，我们建议使用人工智能，特别是时间序列预测技术，预测未来甲氧西林耐药金黄色葡萄球菌(MRSA)的爆发。感染发病率预测是一个基于特征选择的时间序列预测问题，使用由金黄色葡萄球菌、甲氧西林敏感和MRSA感染的发病率、流感发病率和左氧氟沙星和奥司他韦抗菌药物的总治疗天数组成的多变量时间序列进行预测。数据从2009年1月至2018年1月从西班牙赫塔菲大学医院收集，以月为时间粒度。主要工作如下：包装器特征选择方法的应用，其中搜索策略基于多目标进化算法(MOEA)，评估器基于最强大的最先进的回归算法。使用均方根误差(RMSE)和平均绝对误差(MAE)性能指标来衡量特征选择方法的性能。为了选择最满意的预测模型，提出了一种新的多准则决策过程，该方法利用上述指标以及模型预测线的斜率来提前1、2和3步预测。多标准决策过程被应用于通过多个统计测试获得的数据库和回归算法的排名所产生的最佳模型。最后，据我们所知，这是第一次提出基于特征选择的多变量时间序列方法来预测抗生素耐药性。最终结果表明，根据所提出的多准则决策过程的最佳模型对于提前1步、2步和3步的预测，RMSE=(0.1349，0.1304，0.1325)，MAE=(0.1003，0.096，0.0987)。
Antimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, specifically Time Series Forecasting techniques, for predicting future outbreaks of Methicillin-resistant Staphylococcus aureus (MRSA). Infection incidence forecasting is approached as a Feature Selection based Time Series Forecasting problem using multivariate time series composed of incidence of Staphylococcus aureus Methicillin-sensible and MRSA infections, influenza incidence and total days of therapy of both of Levofloxacin and Oseltamivir antimicrobials. Data were collected from the University Hospital of Getafe (Spain) from January 2009 to January 2018, using months as time granularity. The main contributions of the work are the following: the applications of wrapper feature selection methods where the search strategy is based on multi-objective evolutionary algorithms (MOEA) along with evaluators based on the most powerful state-of-the-art regression algorithms. The performance of the feature selection methods has been measured using the root mean square error (RMSE) and mean absolute error (MAE) performance metrics. A novel multi-criteria decision-making process is proposed in order to select the most satisfactory forecasting model, using the metrics previously mentioned, as well as the slopes of model prediction lines in the 1, 2 and 3 steps-ahead predictions. The multi-criteria decision-making process is applied to the best models resulting from a ranking of databases and regression algorithms obtained through multiple statistical tests. Finally, to the best of our knowledge, this is the first time that a feature selection based multivariate time series methodology is proposed for antibiotic resistance forecasting. Final results show that the best model according to the proposed multi-criteria decision making process provides a RMSE = (0.1349, 0.1304, 0.1325) and a MAE = (0.1003, 0.096, 0.0987) for 1, 2, and 3 steps-ahead predictions.