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Antibiotic Selection

Antibiotic Selection

Development of Antibiotic Resistance

Development of Antibiotic Resistance

Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...

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Related Experiment Video

Updated: Sep 22, 2025

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

Published on: September 27, 2016

Personalized antibiograms for machine learning driven antibiotic selection.

Conor K Corbin¹, Lillian Sung², Arhana Chattopadhyay¹

¹Center of Biomedical Informatics Research, Stanford University, Stanford, CA USA.

Communications Medicine

|May 23, 2022

Summary

This summary is machine-generated.

Machine learning-powered personalized antibiograms improve antibiotic prescribing, maintaining coverage while enabling narrower antibiotic selection. This supports antibiotic stewardship and combats resistance by reducing broad-spectrum drug use.

Keywords:

Antibiotics Bacterial infection Disease prevention Epidemiology

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Last Updated: Sep 22, 2025

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Published on: September 27, 2016

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Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes

Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes

Published on: March 3, 2023

Area of Science:

Medical Informatics
Infectious Diseases
Machine Learning

Background:

Antibiotic resistance is a growing public health threat.
Antibiotic prescribing stewardship is crucial for combating resistance.
Clinicians face a trade-off between broad and precise antibiotic coverage.

Purpose of the Study:

To investigate the utility of machine learning-based clinical decision support for antibiotic prescribing stewardship.
To develop and evaluate machine learning models for predicting antibiotic susceptibility patterns (personalized antibiograms).

Main Methods:

Retrospective multi-site study using electronic health record data.
Development of machine learning models to predict personalized antibiograms.
Assessment of antibiotic prescribing trade-offs using linear programming.

Main Results:

Personalized antibiograms showed similar coverage to clinicians in Stanford data (85.9% vs. 84.3%).
Personalized antibiograms significantly improved coverage in Boston data (90.4% vs. 88.1%).
Personalized antibiograms enabled narrower antibiotic prescriptions while maintaining coverage.

Conclusions:

Precision empiric antibiotic prescribing with personalized antibiograms can enhance patient safety.
This approach supports antibiotic stewardship by reducing unnecessary broad-spectrum antibiotic use.
Reducing broad-spectrum antibiotic use is vital to combatting antibiotic resistance.