Predicting Resistance in Pseudomonas aeruginosa With Machine Learning

  08 October 2019

When dealing with multidrug-resistant pathogens, every minute counts and any way to speed up the prediction of antimicrobial resistance (AMR) is key to reducing morbidity and mortality.

Infections with multidrug-resistant (MDR) Pseudomonas aeruginosa are increasing worldwide, and the organism is especially prevalent in health care-associated settings. But rapid AMR predictions could help with providing optimal care to patients.

Further reading: Contagion Live
Author(s): Alexandra Ward
Smart Innovations  
Back

OUR UNDERWRITERS

Unrestricted financial support by:

Antimicrobial Resistance Fighter Coalition

Bangalore Bioinnovation Centre

INTERNATIONAL FEDERATION PHARMACEUTICAL MANUFACTURERS & ASSOCIATIONS

BD





AMR NEWS

Your Biweekly Source for Global AMR Insights!

Stay informed with the essential newsletter that brings together all the latest One Health news on antimicrobial resistance. Delivered straight to your inbox every two weeks, AMR NEWS provides a curated selection of international insights, key publications, and the latest updates in the fight against AMR.

Don’t miss out on staying ahead in the global AMR movement—subscribe now!

Subscribe

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

Keep me informed