Machine Learning Assisted Electrochemical Profiling to Provide Early Identification of Bloodstream Infections Pathogens

In the context of a bacteremia, although significant progress has been made in speeding up pathogen identification once a blood culture bottle turns positive, few cost-effective solutions have been proposed to improve the earlier stages of the process-specifically, from blood collection to bottle positivity. The investigators propose that transport time could be leveraged to grow and identify bacteria, enabling faster access to actionable results through innovative technologies. This project aims to develop a bacterial identification database by analyzing the electrochemical profile of bacteria growing within the blood culture bottle, using machine learning.