Abstract：
Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept proposed by the World Health Organization (WHO). Water and wastewater are among the most important environmental media of AR sources, where the phenomena are generally non-linear. Therefore, the aim of this study was to investigate the application of machine learning-based methods (MLMs) to solve AR-induced problems in water and wastewater. For this purpose, most relevant databases were searched in the period between 1987 and 2023 to systematically analyze and categorize the applications. Accordingly, the results showed that out of 12 applications, 11 (91.6%) were for shallow learning and 1 (8.3%) for deep learning. In shallow learning category, n = 6, 50% of the applications were regression and n = 4, 33.3% were classification, mainly using artificial neural networks, decision trees and Bayesian methods for the following objectives: Predicting the survival of antibiotic-resistant bacteria (ARB), determining the order of influencing parameters on AR-based scores, and identifying the major sources of antibiotic resistance genes (ARGs). In addition, only one study (8.3%) was found for clustering and no study for association. Surprisingly, deep learning had been used in only one study (8.3%) to predict ARGs sequences. Therefore, working on the knowledge gaps of AR, especially using clustering, association and deep learning methods, would be a promising option to analyze more aspects of the related problems. However, there is still a long way to go to consider and apply MLMs as unique approaches to study different aspects of AR in water and wastewater.
摘要：
抗生素耐药性（AR）被认为是本世纪最大的全球威胁之一。只有当人类所有相互联系的区域，动物和环境被视为世界卫生组织（WHO）提出的“一个健康”概念的一部分。水和废水是AR源最重要的环境介质之一，其中现象通常是非线性的。因此,这项研究的目的是研究基于机器学习的方法（MLM）在水和废水中解决AR引起的问题的应用。为此,在1987年至2023年期间搜索了大多数相关数据库，以对应用程序进行系统分析和分类。因此，结果表明，在12个应用程序中，11（91.6％）用于浅层学习，1（8.3％）用于深度学习。在浅层学习类别中，n=6，50%的应用为回归，n=4，33.3%为分类，主要使用人工神经网络，决策树和贝叶斯方法实现以下目标：预测抗生素抗性细菌(ARB)的存活，确定影响参数对基于AR的分数的顺序，并确定抗生素抗性基因（ARGs）的主要来源。此外,只有一项研究(8.3%)被发现用于聚类,没有发现相关性研究.令人惊讶的是,深度学习仅在一项研究(8.3%)中用于预测ARGs序列。因此,研究AR的知识差距，特别是使用聚类，联想和深度学习方法，将是一个有希望的选择，以分析更多方面的相关问题。然而,还有很长的路要走，以考虑和应用MLM作为研究水和废水中AR的不同方面的独特方法。
