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


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.


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


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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