PhD Study : AI for Big Data in Diabetes

Summary

Diabetes has a major impact on health and when untreated can lead to severe organ damage. There are number of factors linked to the disease, ranging from more established ones to those that are less recognised. With the change in lifestyle, environmental factors, and socio-economy status, the risk of developing diabetes has risen. Emerging research indicates that self-management and self-care supported by timely and needful intervention can be instrumental in diabetes care. The interplay between different factors has a major role in diabetes progression. With the rise of Big Data and Artificial Intelligence (AI) applications in healthcare, it is possible to look at the problem from multiple perspectives and integrate different heterogeneous data sources for early detection and prevention of diabetes.

Phase 1 of this project will integrate, analyse, and model existing heterogeneous healthcare data and additional open data to build more robust data-pipelines and AI algorithms to produce evidence-based and interpretable AI models for actionable information. MIDAS successfully delivered an Open-Source-Foundation Demonstrator Platform. This project will adopt the architecture on the NI-High-Performance-Computing (NI-HPC) to securely store the anonymised and synthetic data collated for study. This data integration and mapping on the HPC clusters will create a secure data-pipeline accessibility while ensuring key aspects of data quality, ethics, and governance are met.

The project will design data ingestion pipelines that can quickly integrate structured data from any digital source and convert these data sources into a common format for analysis. Initial work will examine the theoretical linkage between existing clinical indicators (like HbA1C) and lifestyle data to develop AI models that can help in constructive informed policies and prevent the development and progression of diabetes. The Honest Broker Service will be used to access and bring together identified necessary datasets (e.g., Clinical data, TUDA, GPIP) together to test the hypothesis at the core of the proposal, and describe and manage the ethical constraints and governance mechanisms required to use these datasets. The output of the work will be adapted for robust data pipelines and AI algorithms and actionable information for the self-management of diabetes.

For Phase 2 our clinical lead will provide access to patients for investigation at the group level for behavioural changes and how these patients have or have not shifted in behaviour, identified as behavioural drift within the AI models. The patients will be grouped based on attributes such as age, gender, diabetes level, and other stereotypes related to diabetes, approved by our ethical advisor. The work will investigate what measures and interventions were provided to a diabetic patient and what happened to the patient during the treatment.

The outcome of the work is to prevent long-term complications and encourage positive behavioural changes occurring over time among different groups of patients. The research will help to understand the link between their lifestyle behaviour and the risk of having diabetes, hence reducing the associated risk by timely intervention. The findings will inculcate best practice pathways to reduce the number of cases and complications related to diabetes.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain
• Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living