AMR PREDICTION

Antimicrobial resistance (AMR) is one of the most urgent global health challenges of the 21st century. It threatens the effectiveness of antibiotics used to treat bacterial infections, leading to higher rates of complications, prolonged hospital stays, and increased mortality. Reliable, large scale surveillance data are essential for tracking resistance trends and designing effective responses.
This research, aims to analyze anonymized AMR surveillance data available through the Vivli platform using advanced machine learning and deep learning techniques. By applying these computational approaches, the study seeks to identify patterns in resistance development across different pathogens, regions, and timeframes, as well as to detect early signals of emerging resistance.
The objectives of this study are to:
• Develop predictive models to support improved patient outcomes by enabling more accurate and timely selection of effective antibiotics.
• Contribute to antimicrobial stewardship by identifying trends in inappropriate or excessive antibiotic use, helping clinicians tailor therapies based on data-driven insights.
• Provide actionable evidence to inform public health practice, including the identification of high risk regions and populations, to support targeted interventions and surveillance programs.
• Help strengthen health systems by offering scalable, data-driven tools that can support long-term planning in diagnostic support, resource allocation, and infection control strategies.
The insights generated will support ongoing global efforts to combat AMR by bridging computational science with clinical and public health needs. Findings will be shared through peer reviewed publications and will contribute to the global knowledge base for evidence based decision making in infectious disease management.