Identify the application of computational methods and algorithms reported in the literature based on four main categories including data mining, clinical decision support systems, geographical information systems, and digital dashboards and to summarize them in a qualitative scoping review. A scoping review was presented following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, Emerald, Scopus, and Google Scholar databases were searched in July 2016 using uniform keywords for documents that discuss data mining and knowledge discovery, dashboards, geographical information systems, and electronic surveillance of antimicrobial resistance in surveillance systems. Our study mainly focused on knowledge discovery and visualization algorithms, methods, and techniques used in antimicrobial resistance surveillance systems. Thirteen of the reviewed articles applied algorithms to the data mining process. A comparative table of data elements in the reviewed studies was extracted. The characteristics of antimicrobial dashboards were discussed. Heat maps were the most popular method used to visualize the intensity of resistance. Comparative tables are provided in each section of this paper. Data mining, Decision Support Systems, Geographic Information Systems, and dashboards can be integrated for data analysis and to better solve decision support problems. Bio-surveillance systems should be designed and analyzed based on four categories: data mining, dashboards, geography information system, and decision support modules. Furthermore, some questionnaires and checklists were developed and validated to capture related Business Intelligence and analytical requirements. Future studies should focus on developing fast, flexible, and accurate computational bio-surveillance systems by appropriate selecting and applying the considered methods and algorithms.

中文翻译：

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抗菌素耐药性监测系统中的知识发现和可视化：范围审查

根据四个主要类别（包括数据挖掘、临床决策支持系统、地理信息系统和数字仪表板）确定文献中报告的计算方法和算法的应用，并在定性范围审查中对其进行总结。根据系统评价和元分析的首选报告项目指南进行了范围界定。MEDLINE、Emerald、Scopus 和 Google Scholar 数据库于 2016 年 7 月使用统一关键字搜索讨论数据挖掘和知识发现、仪表板、地理信息系统和监测系统中抗菌素耐药性电子监测的文档。我们的研究主要集中在知识发现和可视化算法、方法、和用于抗菌素耐药性监测系统的技术。13 篇评论文章将算法应用于数据挖掘过程。提取了审查研究中数据元素的比较表。讨论了抗菌仪表板的特性。热图是用于可视化阻力强度的最流行方法。本文的每个部分都提供了比较表。可以集成数据挖掘、决策支持系统、地理信息系统和仪表板进行数据分析并更好地解决决策支持问题。生物监测系统的设计和分析应基于四类：数据挖掘、仪表板、地理信息系统和决策支持模块。此外，开发并验证了一些问卷和清单，以获取相关的商业智能和分析要求。未来的研究应该专注于通过适当的选择和应用所考虑的方法和算法来开发快速，灵活和准确的计算生物监测系统。