Diabetes mellitus is characterised by chronic hyperglycaemia associated with a higher risk of cardiovascular complications. Regular monitoring and management of risk factors such as Glycaemic control, blood pressure and lipids and maintaining it within the recommended range is critical to control the disease progression. This constant monitoring generates a large amount of intra-individual longitudinal observations of blood glucose levels: this information can be used to predict diabetes-related multiple complications.
Recently, the rapid development of machine learning methods has resulted in their applications in various areas of healthcare-related research. This PhD project aims to apply different statistical models and machine learning algorithms (including k-nearest neighbour, classification and regression trees, and supervised principal component analysis) to predict various diabetes-related complications, with the aim to develop tools to create a personalized decision system. The post holder will undertake different statistical analyses using the Clinical Practice Research Datalink (CPRD) database, which includes anonymized patient data from a network of GP practices across England, to identify key features (i.e., age, gender, ethnicity, and diabetes duration), which contribute to the risk of diabetes complications.
The student will be embedded within a team of experts in clinical diabetes, epidemiology, and statistics, and receive training in a broad range of statistical methods used to investigate cross-sectional and longitudinal real-world data, as well as methods for prognostic research (development and validation of predictive models) using machine learning and statistical modelling approaches.
The Ph.D. project will be integrated into a vibrant postgraduate research community within the Real-World Evidence Unit and the Diabetes Research Centre, University of Leicester, and help advance the aims of the National Institute of Health and Care Research Leicester Biomedical Research Centre (BRC) and East Midlands Collaboration for Leadership in Applied Health Research and Care (ARC).
Entry requirements
Applicants are required to hold a UK Bachelor Degree 2:1 (or overseas equivalent) or better and a Master’s degree in Statistics, Biostatistics, Data Science, Machine learning or Epidemiology.
The University of Leicester English language requirements apply where applicable.
Project enquiries
Dr Atanu Bhattacharjee ([Email Address Removed])
Dr Francesco Zaccardi ([Email Address Removed])
Eligibility
Open to UK (Home) applicants only.
How to Apply
To submit your application please follow the full guidance at: https://le.ac.uk/study/research-degrees/funded-opportunities/hs-diabetes-bhattacharjee
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