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MRC DiMeN Doctoral Training Partnership: Interpretable machine learning for in-situ healthcare delivery via wearable devices

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  • Full or part time
    Dr G Panoutsos
    Dr I Esnaola
    Dr M van de Werken
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Sleep is a cornerstone of physical and mental health (recognised by the NHS), yet one-in-three people suffer from a sleep issue (CDC). An increasingly recognised problem in medicine is that health issues are multifaceted and require personalisation. When treating sleeping problems this is especially true as sleep timing, needs and physiological and psychological barriers show strong between-individual differences. Moreover, the sole, but costly, treatment solution to sleeping problems, Cognitive Behavioural Therapy for insomnia (CBTi) effectiveness relies on personalisation. In this project, we digitally innovate our product, SleepCogni, to provide a credible solution to a growing societal health problem: poor sleep.

The core of our proposal, are data and advanced sensing technologies and analysis, underpinned by machine learning to assist in personalisation of diagnosis and treatment, and clinical decision-making. Our argument is that, while population-based models dominate the information and decision-making in the area of sleep, it is actually personalised treatment that has shown to improve patient outcomes strongly. The project will conduct research on interpretable machine learning algorithms, suitable clinically-assisted decision making. Interpretability of the key processes behind decisions, is – we believe – what can make a step change in the healthcare sector in terms of better understanding and to a certain extend trust ML-based decisions.

Key topics of this PhD research work will be, interpretability in Machine Learning: Algorithms for transparent and interactive decision making, use of information theory to assess information quality, redundancy and value in the decision making process, application to real-time decisions and personalised healthcare interventions via wearable (medical grade) devices.

Funding Notes

This studentship is part of the MRC Discovery Medicine North (DiMeN) partnership and is funded for 3.5 years. Including the following financial support:
Tax-free maintenance grant at the national UK Research Council rate
Full payment of tuition fees at the standard UK/EU rate
Research training support grant (RTSG)
Travel allowance for attendance at UK and international meetings
Opportunity to apply for Flexible Funds for further training and development
Please carefully read eligibility requirements and how to apply on our website, then use the link on this page to submit an application: https://goo.gl/X5Mhjd

References

De Alejandro Montalvo J, Panoutsos G, Mahfouf M & Catto JW (2015) High Dimensionality and Scaling-up Performance of RBF Models with Application to Healthcare Informatics. International Journal of Machine Learning and Computing, 5(1), 62-67.

Memmolo P, Esnaola I, Finizio A, Paturzo M, Ferraro P & Tulino AM (2012) A new algorithm for digital holograms denoising based on compressed sensing. Proceedings of SPIE - The International Society for Optical Engineering, Vol. 8429

Maan van der Werken et al. (2013), Short-wavelength attenuated polychromatic white light during work at night: limited melatonin suppression without substantial decline of alertness, J. Chronobiology, 30(7), pp. 843-854

How good is research at University of Sheffield in General Engineering?

FTE Category A staff submitted: 21.80

Research output data provided by the Research Excellence Framework (REF)

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