Data challenge: Use of AI to model WHO priority pathogens and antibiotics from Africa using ATLAS dataset

Our research focuses on leveraging machine learning to uncover knowledge from the ATLAS dataset from Africa, aiming to enhance public health practices and improve the health system on the continent. By analyzing the antimicrobial resistance (AMR) dataset using machine learning techniques, we seek to identify correlations, patterns, and trends that can inform targeted interventions. We plan to develop AI models using the Vivli ATLAS dataset and validate them with data from St. Joseph's Hospital Maracha in Uganda. Our research prioritizes pathogens identified by the WHO's Global Antimicrobial Resistance Surveillance System (GLASS), including Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, Staphylococcus aureus, Streptococcus pneumoniae, and Salmonella spp. The antibiotics of focus are ceftriaxone, metronidazole, ciprofloxacin, and amoxicillin. By utilizing AI to predict resistance patterns of WHO priority pathogens to these antibiotics, we aim to support antimicrobial stewardship. AI algorithms can analyze large volumes of AMR data to identify patterns, trends, and risk factors associated with antibiotic resistance. This analysis can detect emerging resistance patterns and predict future trends, enabling the development of targeted interventions and policies to optimize antibiotic prescribing practices and curb the spread of resistant pathogens. Integrating diverse data sources, such as clinical data, surveillance data, and genotype data, allows AI algorithms to provide real-time insights into AMR dynamics. Public health officials can use these insights to implement timely interventions, efficiently allocate resources, and design targeted awareness campaigns. AI can also highlight areas for improvement, such as optimizing antibiotic usage, enhancing infection control protocols, and identifying gaps in healthcare infrastructure. This data-driven approach informs policy decisions, resource allocation, and system-level interventions, fostering more resilient and efficient health systems. Ultimately, the integration of AI techniques with AMR data analysis has the potential to drive evidence-based decision-making and significantly impact public health outcomes.