Acta Scientific Microbiology (ISSN: 2581-3226)

Short Communication Volume 6 Issue 5

Analyzing Antibiotic Resistance: The Role of Statistics and Artificial Intelligence

Ahmed M Saleem*

Gifted School of Basra, Basra, Iraq

*Corresponding Author: Sriram Padmanabhan, Head, R&D, SAVA Healthcare Limited, Research Center, MIDC, Chinchwad, Pune, India.

Received: April 10, 2023; Published: April 17, 2023

Abstract

Antibiotic resistance is a growing concern in the field of public health, and statistical and artificial intelligence (AI) tools can play an important role in understanding and addressing this problem. In this essay, I will discuss how statistics and AI can be used as analysis tools for bacterial resistance to antibiotics.

References

  1. Heuer H., et al. “Antibiotic resistance gene spread due to manure application on agricultural fields”. Current Opinion in Microbiology 3 (2011): 236-243.
  2. Braga AC., et al. “Decision trees as a tool for exploring antibiotic resistance patterns in Escherichia coli isolates from human and animal sources”. PloS One2 (2019): e0212050.
  3. Quan Y., et al. “Deep learning for predicting antibiotic resistance in Escherichia coli based on whole genome sequencing”. Frontiers in Microbiology 11 (2021): 602517.
  4. Rhee C., et al. “Effect of a stewardship intervention on adherence to uncomplicated cystitis and pyelonephritis guidelines in an emergency department setting”. PloS One4 (2018): e0194989.

Citation

Citation: Ahmed M Saleem. “Analyzing Antibiotic Resistance: The Role of Statistics and Artificial Intelligence". Acta Scientific Microbiology 6.5 (2023): 55.

Copyright

Copyright: © 2023 Ahmed M Saleem. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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