Antimicrobial Resistance and Machine Learning: Challenges and Opportunities

Eyad Elyan* (Corresponding Author), A. Hussain, Aziz Sheikh, Pattaramon Vuttpittayamongkol, Karolin Hijazi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
3 Downloads (Pure)


Antimicrobial Resistance (AMR) has been identified by the World Health Organisation (WHO) as one of the top ten global health threats. Inappropriate use of antibiotics around the world and in particular in Low-to-Middle-Income Countries (LMICs), where antibiotics use and prescription are poorly managed, is considered one of the main reasons for this problem. It is projected that the COVID-19 pandemic will accelerate the threat of AMR due to the increasing use of antibiotics across the world, and especially in countries with limited resources. In recent years, machine learning-based methods showed promising results and proved capable of providing the necessary tools to inform antimicrobial prescription and combat AMR. This timely paper provides a critical and technical review of existing machine learning-based methods for addressing AMR. First, an overview of the AMR problem as a global threat to public health, and its impact on countries with limited resources (LMICs) are presented. Then, a technical review and evaluation of existing literature that utilises machine learning to tackle AMR are provided with emphasis on methods that use readily available demographic and clinical data as well as microbial culture and sensitivity laboratory data of clinical isolates associated with multi-drug resistant infections. This is followed by a discussion of challenges and limitations that are considered barriers to scaling up the use of machine learning to address AMR. Finally, a framework for accelerating the use of AMR data-driven framework, and building a feasible solution that can be realistically implemented in LMICs is presented with a discussion of future directions and recommendations.
Original languageEnglish
Pages (from-to)31561 - 31577
Number of pages17
JournalIEEE Access
Early online date16 Mar 2022
Publication statusPublished - 2022

Bibliographical note

The authors would like to thank Global Challenge Research Fund (GCRF) for supporting this work


  • AMR
  • Antimicrobial resistance
  • Machine learning
  • LMICs


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