Abstract：
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.
摘要：
抗菌素耐药性（AMR）增加带来的全球健康危机隐含地需要基于“一个健康”方法的解决方案。然而，多部门，关于AMR的多学科研究很少，并且存在巨大的知识空白来指导综合行动。部分原因是由于已发布信息的大规模和多样性，从未对过去的研究活动进行全面调查。在这里，我们使用人工智能(AI；即，自然语言处理,NLP）创建数据库和信息检索系统的方法，以对过去20年的研究进行知识提取。创建了描述区域的全球地图，方法论,和部门AMR研究活动，证实已经进行了有限的部门间研究，这是指导科学知情的AMR政策解决方案的关键，特别是在低收入国家（LIC）。Further,我们表明，迫切需要跨部门和地区的研究方法更加统一。例如,AMR研究部门之间使用的分析方法的差异，例如采用基于培养的方法与基于基因组的方法，导致部门之间沟通不畅，部分解释了为什么一个基于健康的解决方案不会随之而来。因此,我们的分析表明,在所有行业中同时进行基于文化和基因组的AMR分析对于数据整合和整体OneHealth解决方案至关重要.最后,应优先考虑增加对LIC能力发展的投资，因为它们是AMR负担通常最大的地方。我们的开放式数据库和人工智能方法可以用来进一步开发，传播,并为AMR知识和信息共享创建新的工具和实践。
