This project opportunity is offered as part of the Queen's Doctoral Training Programme - Multi-dimensional approaches to understanding microbe/host interactions in the context of disease, therapeutics and community resilience. For more information, please visit: https://www.findaphd.com/phds/program/queen-s-doctoral-training-programme-multi-dimensional-approaches-to-understanding-microbe-host-interactions-in-the-context-of-disease-therapeutics-and-community-resilience/?p4840
It has been estimated that bloodstream infection (BSI) affects approximately 30 million people with 6 million deaths globally. Gram-negative bacteria (e.g. Klebsiella pneumoniae, E. coli) BSI are associated with more than 30% of hospital-acquired infections. Despite multi-resistant Gram-negative bacteria being one of the greatest threats to global public health, there have been no recent developments in new antibiotics for Gram-negative bacteria. The 2018 World Health Organisation Global Antimicrobial Resistance and Use Surveillance System (GLASS) report showed the global antimicrobial resistance (AMR) varies significantly within and between country. The GLASS report also highlighted many antibiotics are now less effective for treatment. Prescribing appropriate antibiotics plays a key role in tackling emerging AMR. The threat of accelerating AMR has been a major concern in the post-COVID era. The application of machine learning (ML) techniques has been successfully applied to predict diagnosis, outcomes, and disease progression. By applying ML techniques, some clinical factors have been identified to be associated with the risk of developing resistance to antibiotics. However, most of these associations have been identified from small patient cohorts and/or single hospital. The overall aim of this project is to apply ML algorithms to determine appropriate empirical antibiotic use and predict the risk of developing resistance in patients with Gram-negative BSI using two national hospital-based databases in Northern Ireland and Hong Kong. Utilising two large national datasets will provide an international comparison and enable the development of accurate ML prediction models on empirical antibiotic selection for Gram-negative BSI treatment.
Applicants should have a 1st or 2.1 honours degree (or equivalent) in a relevant subject including Pharmacy, Pharmaceutical Sciences, Statistics, Mathematics, Computer Science or a closely related discipline. Students who have a 2.2 honours degree and a Master’s degree may also be considered, but the School reserves the right to shortlist for interview only those applicants who have demonstrated high academic attainment to date.
Start Date: October 2021, Duration: 3.5 years
Applicants must submit an online application through the Queen’s Direct Applications Portal: https://dap.qub.ac.uk/portal/user/u_login.php
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