Chronic obstructive pulmonary disease (COPD) is a progressive lung disease requiring timely diagnosis, treatment and health care support to prevent disease progression. The National Institute for Health and Care Excellence (NICE) estimated approximately 1 million people in the UK with diagnosed COPD and another 2 million with undiagnosed COPD. It is the fourth leading cause of death worldwide and fifth in the UK. The estimated annual cost of managing COPD is £1.9 billion; thus, it incurs a significant burden on healthcare resources and shows an increasing trend due to COPD emergency admissions.
While smoking is the most crucial risk factor for COPD, several comorbid and pathophysiological conditions and environmental factors are linked to COPD-associated hospital admissions in the UK. For clinicians, evaluating the frequency and length of health care resource utilisations (HCRU) are vital toward identifying strategies for managing the disease and preventing its further progression.
However, COPD is clinically heterogeneous; standard clinical measures and phenotyping may not assess the risk fully. The electronic health record (EHR) is deemed a potential resource for decision-making processes and risk assessment. The National Health Service (NHS) routinely manages multiple sources of linked EHR of patients, for example, demography, GP visits, pathophysiology and biochemical parameters, pharmacy, hospital and emergency admissions. More importantly, it is possible to identify longitudinal cohorts of patients with the exact date of exacerbation events, which provides valuable information on disease dynamics: a critical element for a heterogeneous disease like COPD.
Harnessing these multiple sources of primary health care information and adopting appropriate strategies tailored to the individual patient could deliver a personalised medicine-based approach to the management of COPD and reduce the burden on healthcare resources. However, there are several methodological and technical challenges as these would involve the integration of high-volume and high-dimensional complex multi-source data and the application of diverse mathematical and statistical methodologies. In collaboration with an industry partner, we have recently studied the association of baseline patient-level demography, physical, clinical and other pathophysiological predictors with the frequency and rate of exacerbations among COPD patients. The proposed PhD project will extend the current research further to developing predictive analytical tools to identify the risk of severe exacerbations integrating multiple sources of EHR data with advanced statistical models.
The project will use novel methodological techniques to establish the complex relationship between multiple sources of COPD-related EHR data. It will evaluate the distributional properties of the data, explore novel predictive modelling strategies in conventional and diverse machine learning and deep learning modelling frameworks. An overall aim is to produce an optimised protocol that harmonises the use of EHR data and maximises the reproducibility of outcomes and ultimately improves clinical practice. The project will target several critical gaps in the current statistical methodologies with implications in broader disease contexts.
The project will provide an excellent opportunity for a prospective student to work in one of the most active and exciting areas of medical research. The student will acquire essential skills and knowledge of handling high-volume and high-dimensional EHR data and apply novel and advanced statistical, mathematical, and computational methodologies to develop predictive analytical tools integrating these complex datasets. Previous background in employing statistical models and machine learning tools in R, Python or similar programming environments will be required.
Candidates should contact the lead supervisor (Dr Mintu Nath) to discuss the project in advance of submitting an application, as supervisors will be expected to provide a letter of support for suitable applicants. Candidates will be informed after the application deadline if they have been shortlisted for interview.
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APPLICATION PROCEDURE:
International applicants are eligible to apply for this studentship but will have to find additional funding to cover the difference between overseas and home fees (approximately £17,000 per annum)
- Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php
- You should apply for the Degree of Doctor of Philosophy in Applied Health Sciences to ensure your application is passed to the correct team
- Please clearly note the name of the supervisor and exact project title on the application form. If you do not mention the project title and the supervisor on your application it will not be considered for the studentship.
- Candidates should have (or expect to achieve) a minimum of a First Class Honours degree in a relevant subject. Applicants with a minimum of a 2:1 Honours degree may be considered provided they have a Distinction at Masters level.
- General application enquiries can be made to [Email Address Removed]
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