Machine learning to predict antimicrobial resistance: future applications in clinical practice?

  14 February 2024

This review explores the use of machine learning (ML) in predicting antimicrobial resistance (AMR). The review included 36 studies, primarily based on hospital data and outpatient data, with the majority conducted in high-resource settings. The studies focused on predicting drug resistance in infected patients, ML-assisted antibiotic prescription, colonization with carbapenem-resistant bacteria, and national and international AMR trends. The most common inputs were demographic characteristics, previous antibiotic susceptibility testing, and prior antibiotic exposure. 92% of the studies targeted Gram-negative bacteria resistance prediction. The review concludes that ML can potentially aid in AMR prediction, but future research is needed to design, implement, and evaluate the effectiveness of ML decision support systems.

Further reading: Infectious Diseases Now
Author(s): Yousra Kherabi et al
Smart Innovations  
Back

OUR UNDERWRITERS

Unrestricted financial support by:

Antimicrobial Resistance Fighter Coalition

Bangalore Bioinnovation Centre

INTERNATIONAL FEDERATION PHARMACEUTICAL MANUFACTURERS & ASSOCIATIONS

BD





AMR NEWS

Every two weeks in your inbox

Because there should be one newsletter that brings together all One Health news related to antimicrobial resistance: AMR NEWS!

Subscribe

What is going on with AMR?
Stay tuned with remarkable global AMR news and developments!

Keep me informed