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Artificial Intelligence (AI) and Machine Learning (ML) for Continuous Glucose Monitoring (CGM) from Diabetes Patient Blood Data - PhD in Medical Studies (Research England DTP) Ref: 3875

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  • Full or part time
    Prof N Vaughan
    Prof R Andrews
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Project Description

The student will have opportunities to develop skills in AI and ML data analysis developing algorithms for continuous blood-glucose monitors (CGM). The student will develop predictive models for blood glucose responding to exercise activities and meals identified by carbohydrate counting. This could potentially improve patient safety by application to closed-loop insulin pumps.


The student will have opportunities to develop AI and ML techniques for available datasets from diabetes patients using continuous glucose monitoring (CGM) devices which are becoming widely utilised and support DIY looping.

This provides excellent opportunities for a student with strong computing skills to enhance those skills and apply them to an important health related question.

The student will develop predictive algorithms and investigate connecting them with insulin pumps to automate the monitoring of CGM and minimise the occurrence of hypoglycaemia, improving patient safety.

The student will have opportunity to enhance computer algorithm development skills using real-time data from continuous blood glucose monitoring devices perhaps combined with smartphone apps. This could automate, simplify and improve accuracy of predicting the optimum amount of insulin for diabetes patients.

The student will make use of online open-source DIY looping systems developed by patients who adapt their own versions of open-source looping code. This creates a “closed loop” typically through a smartphone app. The student will make use of online systems such as Nightscout enabling a patient’s blood glucose to be remotely monitored, which could be useful when someone is asleep or for monitoring children when away from parents.

The student will focus on blood glucose monitoring which is a critical element for patients with diabetes. Patients keep track of their blood glucose regularly as it is affected by events including meals and exercise. Monitoring blood glucose is useful to detect when insulin is required to stay within the acceptable range avoiding hypoglycaemia.

The student will work with the newest generation of continuous blood glucose monitors including Freestyle Libre, Medtronic Dexcom and Omnipod. CGMs generate regular data and unlike painful finger prick glucose tests, CGMs do not require lancets and could be used whilst driving.

This transformative functionality enables greater flexibility and control, empowering patients.

Funding Notes

Applicants must ensure that they meet the eligibility requirements of the University of Exeter. To qualify for ‘home’ tuition fee status, you must be a UK or EU citizen who has been resident for 3 years prior to commencement.

This is a 3 year fully-funded PhD studentship. Stipends are at an enhanced rate of £17,059 (2020-21) and all Home/EU tuition fees are covered. Funds will also be available for travel and research costs.



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