Clinical application of machine learning algorithms to determine appropriate antibiotic use in children with Gram-negative bacteria infections

Digital health is a growing and productive field in recent years. The application of machine learning (ML) techniques in electronic healthcare databases has been successfully used for clinical diagnosis, outcome prediction, and disease progression. With increasing global antimicrobial resistance (AMR), appropriate antibiotic selection plays a key role to tackle AMR. The overall aim of this project is to develop and apply ML algorithms using global antimicrobial resistance data, to determine appropriate antibiotic use for children.