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
OUR UNDERWRITERS
Unrestricted financial support by:
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Antimicrobial Resistance Fighter Coalition |
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Evotec |
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JSS University |
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INTERNATIONAL FEDERATION PHARMACEUTICAL MANUFACTURERS & ASSOCIATIONS |
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CAPI (Continuous AMR Partnering Initiative) unites Suppliers and Users worldwide with the aim to add to the curbing of AMR.
