Severe infections, common in the ICU, are associated with significant morbidity and mortality Infection management is challenging here due to uncertainties in antibiotic dosing, and increased antibiotic resistance in this population Clinicians are today left with inadequate solutions to appropriately dose many of our antibiotics, and have little guidance on who is at risk of nosocomial infections or infections caused by multidrug resistant pathogens, leading to poor outcomes and unacceptable high use of antibiotics, further compromising lifespan of antibiotics and increasing antimicrobial resistance

This project will develop hybrid machine learning models to find better solutions for these challenges We will first develop models to predict antibiotic concentrations of the antibiotics used most commonly for severe infections, as well as dosing advice for optimal antibiotic activity Secondly, we will develop predictive models for nosocomial infections such as ventilator associated pneumonia and invasive candidiasis, but also for identifying patients at risk of antimicrobial resistant infections Finally, we will make this information available to the healthcare workers at the bedside in order to tailor the treatment to the patient and the infection, as well as have better insights in the risks the patient is exposed to This will allow personalized medicine that will improve outcome and reduce antibiotic resistance in these vulnerable patients