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Review Papers | Computer Science and Information Technology | India | Volume 12 Issue 3, March 2023 | Popularity: 5.4 / 10

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Discovering the Use of Machine Learning and Deep Learning for AntiMicrobial Resistance Detection

Srivaramangai R

Abstract: A major threat to modern medicine is antimicrobial resistance (AMR), a global health crisis. To halt the emergence and further spread of AMR, effective prevention strategies are urgently required. Clinical microbiology currently primarily uses two methods to diagnose AMR. They are Whole - genome sequencing for antimicrobial susceptibility testing (WGS - AST) and traditional culture - based antimicrobial susceptibility testing (AST). Although the development of molecular tests has significantly improved the speed of diagnostic testing as well as the prompt identification of pathogens and patterns of antibiotic resistance, their high costs and limited availability prevent their widespread use. Machine learning (ML) is increasingly being used to predict pathogen resistance to various antibiotics based on gene content and genome composition given the availability of data sets containing hundreds or thousands of pathogen genomes. Predictive models of antimicrobial resistance that are driven by machine learning (ML) and deep learning (DL) may be able to bridge the gap between the results of molecular and genotypic susceptibility analysis and the collection of specimens, making it easier to choose empirical antibiotics in a faster way. The research project aims to promote the use of machine learning (ML) in front - line settings while also highlighting the additional refinements required to use these methods safely and with confidence. The use of machine learning is not new in this field. Thus, in this paper, the review of such research works is done with comparative analysis and inferences.

Keywords: AntiMicronial Resistance, Antibiotic Resistance, Machine Learning, Deep Learning, AMPTrans - lstm, XGBoost

Edition: Volume 12 Issue 3, March 2023

Pages: 1394 - 1397

DOI: https://www.doi.org/10.21275/SR23322231026

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