Title: Integration of streaming data to improve opportunities for antimicrobial treatments in patients with serious respiratory infection
Supervisors:
Prof Paul Dark
Prof Tjeerd Van Staa
Dr T Felton
Description: To develop and evaluate software algorithms to analyse continuously, collected patient level clinical data, laboratory biomarkers of infection, microbiological results, treatment and outcome data for patient and population benefit.
Antibiotics are important medicines for treating bacterial infections in both humans and animals and are losing their effectiveness at an increasing rate. Antibiotic resistance is one of the most significant threats to patients' safety. It is driven by overusing antibiotics. To slow down the development of antibiotic resistance it is important to use antibiotics in the right way, to use the right drug, at the right dose, at the right time for the right duration. There is great need for effective and simple interventions that optimise antibiotic prescribing. A recent review concluded that we need to better understand the quality of interventions in this area and what works best when. The NHS faces very different populations and healthcare setting and these may all respond differently to the introduction of new interventions. But the conventional scientific approach is to evaluate single interventions in well-controlled identical circumstances without capturing the real-world complexity of the NHS. This project will focus on the important clinical challenge of optimising antibiotics in patients with life-threatening infection in hospitals
Our aim through this project is to develop and evaluate software algorithms to integrate relevant, real-world NHS data sources that drive clinical decisions about antimicrobial treatment of hospitalised patients with serious infections.
Funding: Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in Data Science, Mathematics, Statistics, Epidemiology or other degrees with demonstrated and considerable focus on quantitative research method.
Applicants must be from the UK/EU and funding covers fees/stipend for three years commencing September 2018. Applicants may contact the Primary Supervisor directly with any questions.
For more details please see: https://www.findaphd.com/search/ProjectDetails.aspx?PJID=93832
Matthew Sperrin | Senior Lecturer in Health Data Science | Faculty of Biology, Medicine and Health | Vaughan House | University of Manchester | M13 9GB | 0161 30 67629
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