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Antibiotic Selection

Antibiotic Selection

Antimicrobial Effectiveness

Antimicrobial Effectiveness

The effectiveness of antimicrobial agents depends on various factors influencing their ability to eliminate microbial populations. Larger microbial populations require more time for complete eradication, emphasizing the importance of population size analysis when evaluating antimicrobial efficacy.Microbial resistance to antimicrobial agents varies significantly. Highly resilient microorganisms include endospores, gram-negative bacteria, and non-enveloped viruses, while prions are exceptionally...

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Machine Learning-Based Prediction of Antimicrobial Susceptibility: A Step Towards Precision Antimicrobial

Fatemeh Amrollahi¹, Fateme Nateghi Haredasht¹, Arin Vansomphone²

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

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|February 23, 2026

Summary

Machine learning models predict antimicrobial resistance (AMR) using electronic health records. This aids in selecting appropriate antibiotics early, combating the global AMR crisis.

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Biosensor for Detection of Antibiotic Resistant Staphylococcus Bacteria

Biosensor for Detection of Antibiotic Resistant Staphylococcus Bacteria

Published on: May 8, 2013

Area of Science:

Infectious Diseases
Medical Informatics
Computational Biology

Background:

Antimicrobial resistance (AMR) is a major global health threat.
Empirical broad-spectrum antibiotic use and delays in susceptibility data worsen AMR.
Predicting resistance at the time of culture is crucial for effective treatment.

Purpose of the Study:

To develop and validate Machine Learning (ML) models for predicting bacterial antibiotic resistance.
To utilize routinely collected Electronic Health Record (EHR) data for resistance prediction.
To enhance early empirical antibiotic selection and antimicrobial stewardship.

Main Methods:

Developed and validated ML models using EHR data from inpatient and outpatient encounters.
Focused on predicting resistance at the time of blood, urine, or respiratory bacterial culture.
Evaluated model performance, particularly in inpatient settings with more complete data.

Main Results:

ML models demonstrated robust predictive accuracy for antibiotic resistance.
Models identified resistance patterns independently, mirroring clinical reasoning.
Performance was notably strong in inpatient settings.

Conclusions:

ML models using EHR data can accurately predict antibiotic resistance.
Integration into clinical workflows can improve empirical antibiotic selection.
These tools support antimicrobial stewardship and reduce broad-spectrum antibiotic overuse.