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Loughborough University Featured PhD Programmes

Precision Medicine DTP: Data-driven personalised care for Gestational Diabetes


College of Medicine and Veterinary Medicine

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Prof R Reynolds , Dr D Wake , Dr A Manataki , Dr Robert Lindsay No more applications being accepted Competition Funded PhD Project (European/UK Students Only)

About the Project

16% of women in the UK develop high levels of sugar in their blood during pregnancy (gestational diabetes (GDM)) and rates are increasing due to rising obesity levels among women. GDM leads to excessive growth of the baby and problems for the mother and baby during and after delivery (1-3). Lifestyle change; including weight loss and dietary change is key to management of GDM, although some women will also need either tablet or injectable medications (insulin) to maintain good blood glucose control. Women will be expected to monitor blood glucose levels several times a day using home monitoring devices and make regular treatment adjustments. The extra visits to specialist clinics and intensive monitoring/treatment are onerous for women and outcomes could be improved.

This PhD study will investigate how machine learning methods can be used in the setting of a data-driven web-based system for people with diabetes (MyDiabetesMyWay) to deliver data-driven personalised support for GDM, complementing existing services and improving user experiences and care outcomes. Pregnant women are known to be users of digital healthcare and health information already (4): the system will support remote consultation and provide 24/7 access to automated tailored information using data from i) the health record (medications, demographics, medical history, medications, lab results), ii) home recorded (such as glucose, weight and activity readings), and iii) patient reported experience measures (PREMs) to tailor the intervention delivery. This will include the use of machine learning models to drive personalised/ tailored treatment advice around areas ranging from lifestyle change to insulin dosing.

Aims

This project aims to
i) Assess the current published evidence around data-driven predictive modelling in GDM
ii) Assess needs and environmental barriers/ enablers around digital tools to support women with GDM and the health care professionals looking after them.
iii) Develop and validate machine learning models predicting short and medium term outcomes for GDM patient that will support novel state-of-the-art data-driven automated personalised tools linked to decision support, alerts and notifications.
iv) Support linking of machine learning outputs to an online tool in conjunction with MyWay Digital Health, (advisory support from the school of informatics). MWDH to perform rapid-user feedback loops (safety and efficacy testing)
v) Perform a mixed-methods assessment of the model performance/ impact of the tool in clinical practice through an observation study; tracking pregnancy outcomes, metabolic parameters, anthropometric measurements, and determining impact on service efficiency and patient quality of life.

Training outcomes

This project provides a broad training experience in a number of research disciplines ranging with a main focus around data-driven innovation and AI modelling, but will also encompass training in literature review, qualitative research methods, user-centric design, and mixed methods research; with collation and analysis of qualitative and quantitative longitudinal data within a healthcare setting. The successful applicant will benefit from key generic skills training at the start and throughout the project to support critical thinking, writing skills, systematic literature review, statistics and other areas of personal/ career development. In addition, this project provides a unique opportunity to spend time in a commercial environment to understand the process of product development, safety testing and regulatory requirements for digital health translation. The key learning will be around machine learning model development using high frequency (glucose/ activity) data, low frequency clinic observational data and patient reported experience measures to predict improvements in outcomes linked to automated support through end user digital interfaces. Machine learning model development/ data-analytics supervision will be delivery by MyWay Digital health, supported by University of Edinburgh (Dr Areti Manataki). The student will benefit from formidable supervisory experience from national and international experts across clinical, informatics, design and qualitative research domains, and develop skills that will be highly relevant for future academic or commercial job prospects.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2020

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area.

Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,009 (RCUK rate 2019/20) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: http://www.mrc.ac.uk/skills-careers/studentships/studentship-guidance/student-eligibility-requirements/

Enquiries regarding programme: [Email Address Removed]

References

1. Diabetes Uk. What is Gestational Diabetes? Available from : https://www.diabetes.org.uk/diabetes-the-basics/gestational-diabetes [accessed13th March 2020]

2. Farrar D, Simmonds M, Griffin S, et al. The identification and treatment of women with hyperglycaemia in pregnancy: an analysis of individual participant data, systematic reviews, meta-analyses and an economic evaluation. Health Technology Assessment, No. 20.86.

3. Johns EC, Denison FC, Norman JE, Reynolds RM. Gestational Diabetes Mellitus: Mechanisms, Treatment, and Complications. Trends Endocrinol Metab. 2018 Nov;29(11):743-754.

4. Mackillop L et al (2016) Trial protocol to compare the efficacy of a smartphone-based blood glucose management system with standard clinic care in the gestational diabetic population. BMJ Open 6; e009702. doi:10.1136/bmjopen-2015- 009702
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