About the Project
Assisted Self-Management of Type 1 Diabetes Mellitus using Machine Learning and Artificial Intelligence
Supervisors: Dr John Donovan, Dr Konrad Mulrennan and Dr Etain Kiely.
This research programme is being conducted in collaboration with clinicians of a University hospital and an SME that provides advanced IT solutions for the prevention and care of chronic diseases.
Diabetes mellitus is a chronic autoimmune disease and patients are at elevated risk of developing disease specific complications. If these complications are undetected for prolonged periods it can result in serious and negative consequence for the patients. Maintaining narrow control of blood glucose levels is key to managing diabetes and reducing the risk of developing complications.
This research programme will focus on developing artificial intelligence (AI) technologies to assist patients with their management of diabetes. The new AI technologies will empower patients and deliver improved long term care while reducing the risks of developing acute and chronic diabetes related complications.
The candidate will be based in Sligo, Ireland.
The specific aims of the research programme are:
• Apply statistical analysis tools and AI/machine learning (ML) methodologies to analyse individual patient’s data sets for nutrition, activity/exercise, insulin, and blood glucose levels.
• Advance the area of diabetes self-management and care by the application of AI/ML models.
• Assess the viability of AI technologies to assist self-management and care of diabetes patients.
• Develop an AI system to enhance individuals’ ability to manage blood glucose levels and improve overall care and patient outcomes.
The specific objectives of the research programme are:
• Investigate multiple anonymised databases of diabetic patients’ self-recorded data. Assess the databases for anomalies, trends, patterns etc.
• Complete a literature review on diabetes management and care, AI/ML in healthcare, and the ethics and explainability of ML in healthcare.
• Explore the application of explainable AI/ML technologies for diabetes self-management.
• Use exploratory data analysis techniques to assist patients to interpret model outputs.
• Develop and validate an AI/ML model to predict optimal management of diabetes for individual patients.
• Take clinical guidance to ensure development of patient safe technologies.
• Generate academic papers for peer-reviewed conference and journal publications.
Profile of Ideal Candidate
The researcher selected for this position will have a background in medical statistics, statistics, mathematics, science or engineering. Key skills include:
• A 1st class or 2.1 Honours degree in from an appropriate discipline with a substantial statistical component. Experience of working in the in the field would be a distinct advantage.
• Proficient in a computer programming language. Examples include Python, MATLAB, R or C.
• Strong knowledge of statistics, data management and analysis.
• Knowledge of multivariate data analysis is desirable.
• Knowledge of ML techniques and methodologies would be advantageous.
• It is expected that the student will be capable of innovative thinking and problem solving.
• Understanding of qualitative and quantitative research methodologies. Previous experience of research methods and techniques is an advantage but not essential as guidance will be provided.
• Excellent oral, written and analytical skills are essential.
The appointee should have a proven capability to undertake project work, be well motivated with good organizational skills and be capable of working independently under the direction of their supervisors. They should be decisive and thorough in their approach to researching and developing solutions to presented problems.
FUNDING SOURCE: CUA Bursary 2020 with a maintenance grant of €13,000 p.a. and up to €4,000 p.a. for expenses as approved. Tuition fees (currently €819 p.a.) will be paid by the Institute for all holders of student bursaries. Students are required to pay their registration fee. The registration fee for 2018-2019 is €3,000 p.a.
DURATION: Funding will be initially for a maximum of 2 years full time Master’s registration. This research has potential to transition into a PhD up to a maximum of 4 years.
Please send by email to Dr John Donovan, ([Email Address Removed]) and CC Veronica Cawley [Email Address Removed]
1) A detailed cover letter
2) A full curriculum vitae
3) Academic transcripts for each year of study
4) Contact details of two academic referees
Applicants whose first language is not English must submit the original certificate of completion of an English test.
Please insert the subject line ‘IT Sligo CUA Bursary 2020’ with your electronic correspondence.
Closing Date for Applications: 17:00 local time, 21st of September 2020
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