Data-led approach
We're tackling new ways to address AMR at a system-wide level by integrating data flows, and developing and implementing new AIs for infection care. We are pioneering projects such as digital diagnosis of urinary tract infections and prediction of sepsis-related deaths.
Building on work originally supported by NIHR Clinical Research Network, AMR-X Liverpool is supported by funding from the Office for Life Sciences (OLS) and Wellcome via the Centres for Antimicrobial Optimisation Network (CAMO-Net). We work in collaboration with Civic Health Innovation Labs (CHIL) and IQVIA. View a summary in The Lancet.
Our research
- Connects dataflows for clinical care and microbiology results to provide real-time insights in antimicrobial therapy and AMR in collaboration with CIPHA
- harvesting information to develop automated system interventions for development of dashboards and direct patient care.
- Uses state-of-the-art mathematical modelling and data science approaches to develop and implement automated system interventions for:
- Estimation of the prevalence of AMR using Bayesian techniques
- Data driven approaches for antimicrobial susceptibility testing to help achieve national targets agreed at the United Nations General Assembly High Level meeting 2024
- Simulators for complex workflows required for infection care
- Development of new data science tools to power molecular microbiology laboratories
- Health economic analyses for automated system interventions
AMR-X provides a method to integrate multiple sources of data to improve the care of patients receiving antimicrobial therapy. AMR-X proposes linking routinely collected health-care and surveillance data to be used for the benefit of individual patients and the broader population.
Professor William Hope
Key staff
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