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A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria

The Journal of Antibiotics volume 74, pages 838–849 (2021)Cite this article

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Abstract

Antimicrobials have paved the way for medical and social development over the last century and are indispensable for treating infections in humans and animals. The dramatic spread and diversity of antibiotic-resistant pathogens have significantly reduced the efficacy of essentially all antibiotic classes and is a global problem affecting human and animal health. Antimicrobial resistance is influenced by complex factors such as resistance genes and dosing, which are highly nonlinear, time-lagged and multivariate coupled, and the amount of resistance data is large and redundant, making it difficult to predict and analyze. Based on machine learning methods and data mining techniques, this paper reviews (1) antimicrobial resistance data storage and analysis techniques, (2) antimicrobial resistance assessment methods and the associated risk assessment methods for antimicrobial resistance, and (3) antimicrobial resistance prediction methods. Finally, the current research results on antimicrobial resistance and the development trend are summarized to provide a systematic and comprehensive reference for the research on antimicrobial resistance.

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Acknowledgements

The authors would like to acknowledge the financial supports provided by the National Natural Science Foundation of China (No. 61802411).

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  1. College of Information and Electrical Engineering, China Agricultural University, Beijing, PR China

    Xinxing Li, Ziyi Zhang & Buwen Liang

  2. Transportation and Economics Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, PR China

    Fei Ye & Weiwei Gong

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Li, X., Zhang, Z., Liang, B. et al. A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria. J Antibiot 74, 838–849 (2021). https://doi.org/10.1038/s41429-021-00471-w

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