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.

抗菌素耐药性已成为最重要的健康问题之一，全球已提出全球行动计划。预防在这些行动计划中起着关键作用，在这种情况下，我们建议使用人工智能，特别是时间序列预测技术，来预测未来耐甲氧西林金黄色葡萄球菌(MRSA) 的爆发。感染发生率预测被视为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 感染的发生率、流感发生率和左氧氟沙星和左氧氟沙星的总治疗天数组成的多变量时间序列。奥司他韦抗菌素。数据是从 2009 年 1 月至 2018 年 1 月从赫塔菲大学医院（西班牙）收集的，使用月份作为时间粒度。这项工作的主要贡献如下：包装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）以及基于最强大的最先进回归算法的评估器。已使用均方根误差( RMSE ) 和平均绝对误差( MAE )来衡量特征选择方法的性能。) 性能指标。提出了一种新的多标准决策过程，以使用前面提到的指标以及 1、2 和 3 步预测中模型预测线的斜率来选择最令人满意的预测模型。多标准决策过程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产生的最佳模型。最后，据我们所知，这是首次提出基于特征选择的多元时间序列方法用于抗生素耐药性预测。最终结果表明，根据提出的多标准决策过程的最佳模型提供了RMSE  = (0.1349, 0.1304, 0.1325) 和MAE = (0.1003, 0.096, 0.0987) 用于 1、2 和 3 步提前预测。