The global health crisis posed by increasing antimicrobial resistance (AMR) implicitly requires solutions based a One Health approach, yet multisectoral, multidisciplinary research on AMR is rare and huge knowledge gaps exist to guide integrated action. This is partly because a comprehensive survey of past research activity has never performed due to the massive scale and diversity of published information. Here we compiled 254,738 articles on AMR using Artificial Intelligence (AI; i.e., Natural Language Processing, NLP) methods to create a database and information retrieval system for knowledge extraction on research perfomed over the last 20 years. Global maps were created that describe regional, methodological, and sectoral AMR research activities that confirm limited intersectoral research has been performed, which is key to guiding science-informed policy solutions to AMR, especially in low-income countries (LICs). Further, we show greater harmonisation in research methods across sectors and regions is urgently needed. For example, differences in analytical methods used among sectors in AMR research, such as employing culture-based versus genomic methods, results in poor communication between sectors and partially explains why One Health-based solutions are not ensuing. Therefore, our analysis suggest that performing culture-based and genomic AMR analysis in tandem in all sectors is crucial for data integration and holistic One Health solutions. Finally, increased investment in capacity development in LICs should be prioritised as they are places where the AMR burden is often greatest. Our open-access database and AI methodology can be used to further develop, disseminate, and create new tools and practices for AMR knowledge and information sharing.

中文翻译：

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全球抗菌素耐药性研究的特征解释了 One Health 解决方案发展缓慢的原因：基于人工智能的差距分析的应用


抗菌素耐药性 (AMR) 增加所造成的全球健康危机隐含地需要基于“同一个健康”方法的解决方案，但针对抗菌素耐药性的多部门、多学科研究很少，而且在指导综合行动方面存在巨大的知识差距。部分原因是由于已发表信息的规模庞大且多样性，从未对过去的研究活动进行过全面调查。在这里，我们使用人工智能（AI，即自然语言处理，NLP）方法编译了 254,738 篇有关 AMR 的文章，创建了一个数据库和信息检索系统，用于提取过去 20 年研究的知识。创建了全球地图，描述了区域、方法和部门的抗微生物药物耐药性研究活动，这些活动证实了已经开展的有限的跨部门研究，这是指导抗微生物药物耐药性的科学政策解决方案的关键，特别是在低收入国家 (LIC)。此外，我们表明迫切需要跨部门和地区的研究方法进一步协调。例如，AMR 研究中各部门使用的分析方法存在差异，例如采用基于培养的方法与基因组方法，导致部门之间沟通不畅，并部分解释了为什么基于 One Health 的解决方案未能随之而来。因此，我们的分析表明，在所有部门同时进行基于培养和基因组的 AMR 分析对于数据集成和整体 One Health 解决方案至关重要。最后，应优先考虑增加对低收入国家能力建设的投资，因为这些国家的抗菌药物耐药性负担往往最为严重。我们的开放访问数据库和人工智能方法可用于进一步开发、传播和创建 AMR 知识和信息共享的新工具和实践。