Knowledge discovery and visualization in antimicrobial resistance surveillance systems: a scoping review

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.