Predicting Resistance in Pseudomonas aeruginosa With Machine Learning
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.
Display your AMR technology / product:
Global AMR Technologies Database
- Preventive – Diagnostic – Antimicrobial technologies
- Academia – Research Institutes – Start ups – SMEs – Multinationals
- Early research <-> near market (max 5)
- Global reach for funding / co-development / licensing
- No costs (until year end 2019)
Festive Launch Database 19 Nov Symposium Dinner ‘Emerging Antimicrobials and Diagnostics in AMR 2019‘
Click to join the Symposium Dinner (to join the Symposium separate registration obligatory)