Antimicrobial use and resistance

Antimicrobial resistance (AMR) is an ever-increasing concern. The Small Animal Veterinary Surveillance Network (SAVSNET) dataset is being used to develop new strategies to identify and minimise unnecessary antimicrobial usage.

Antimicrobial resistance is a critical challenge for human and animal health that requires a coordinated endeavour across the disciplines of clinical and data science. This project utilises the SAVSNET dataset which has amassed 10 million veterinary electronic health records.

While previous work has been successful for the highly specific targeting of a singular disease or condition, a wider analysis is necessary here. Collaborating with the Artificial Intelligence and Human Systems Group at Durham University, we're striving to integrate novel machine learning strategies and distil them into the emerging field of veterinary bioinformatics.

Recognising the features and signals that might be available in a large companion animal clinical records dataset, the project will develop and apply cutting-edge machine-learning methodologies to derive important insights.

This research is funded by the BBSRC North West Doctoral Programme in Bioscience.