Luz Christian F, van Niekerk J Magnus, Keizer Julia, Beerlage-de Jong Nienke, Braakman-Jansen L M Annemarie, Stein Alfred, Sinha Bhanu, van Gemert-Pijnen J E W C, Glasner Corinna
University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Hanzeplein 1, 9700RB Groningen, the Netherlands.
University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Hanzeplein 1, 9700RB Groningen, the Netherlands; University of Twente, Department of Psychology, Health and Technology, Centre for eHealth and Wellbeing Research, PO Box 217, 7500AE Enschede, the Netherlands; University of Twente, Department of Earth Observation Science (EOS), Faculty of Geo-Information Science and Earth Observation (ITC), PO Box 217, 7500AE Enschede, the Netherlands.
Artif Intell Med. 2022 Jan;123:102216. doi: 10.1016/j.artmed.2021.102216. Epub 2021 Nov 20.
Antimicrobial resistance (AMR) is a global threat to health and healthcare. In response to the growing AMR burden, research funding also increased. However, a comprehensive overview of the research output, including conceptual, temporal, and geographical trends, is missing. Therefore, this study uses topic modelling, a machine learning approach, to reveal the scientific evolution of AMR research and its trends, and provides an interactive user interface for further analyses.
Structural topic modelling (STM) was applied on a text corpus resulting from a PubMed query comprising AMR articles (1999-2018). A topic network was established and topic trends were analysed by frequency, proportion, and importance over time and space.
In total, 88 topics were identified in 158,616 articles from 166 countries. AMR publications increased by 450% between 1999 and 2018, emphasizing the vibrancy of the field. Prominent topics in 2018 were Strategies for emerging resistances and diseases, Nanoparticles, and Stewardship. Emerging topics included Water and environment, and Sequencing. Geographical trends showed prominence of Multidrug-resistant tuberculosis (MDR-TB) in the WHO African Region, corresponding with the MDR-TB burden. China and India were growing contributors in recent years, following the United States of America as overall lead contributor.
This study provides a comprehensive overview of the AMR research output thereby revealing the AMR research response to the increased AMR burden. Both the results and the publicly available interactive database serve as a base to inform and optimise future research.
抗菌药物耐药性（AMR）是对健康和医疗保健的全球性威胁。为应对日益加重的AMR负担，研究资金也有所增加。然而，目前缺少对研究产出的全面概述，包括概念、时间和地理趋势等方面。因此，本研究采用主题建模这一机器学习方法来揭示AMR研究的科学演变及其趋势，并提供一个交互式用户界面以供进一步分析。
对通过PubMed查询获得的包含AMR文章（1999 - 2018年）的文本语料库应用结构主题建模（STM）。建立了一个主题网络，并通过频率、比例以及随时间和空间的重要性来分析主题趋势。
总共从166个国家的158,616篇文章中识别出88个主题。1999年至2018年间，AMR相关出版物增加了450%，突出了该领域的活力。2018年的突出主题包括新兴耐药性和疾病的策略、纳米颗粒以及管理。新兴主题包括水与环境以及测序。地理趋势显示，世界卫生组织非洲区域耐多药结核病（MDR - TB）问题突出，这与MDR - TB负担情况相符。近年来，中国和印度的贡献不断增加，仅次于总体贡献领先的美国。
本研究提供了AMR研究产出的全面概述，从而揭示了AMR研究对不断增加的AMR负担的应对情况。研究结果以及公开可用的交互式数据库都可作为为未来研究提供信息和优化研究的基础。
