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MRC DiMeN Doctoral Training Partnership: Integrating electronic health records and data derived from in silico models, for personalised medicine

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  • Full or part time
    Dr M Villa-Uriol
    Dr M Alvarez
  • 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

Currently, machine learning dominates the formulation of data-driven predictive models and has been successfully used in a diverse range of applications. In particular, predictive probabilistic models have proved successful in various domains (e.g. human body pose estimation from silhouettes) [1,2,3]. The potential impact of these techniques in medicine is yet to be explored.
This PhD project proposes to do that and to advance the state of the art by:
(i) Providing evidence that probabilistic models can predict outcomes using data derived from in silico patient-specific models.
(ii) Developing novel probabilistic predictive models able to integrate data derived from in silico models and other traditional medical data (e.g. patient health records) to improve the prediction of outcomes vs in silico descriptors alone.
The effectiveness of the proposed framework will be evaluated in two case studies (cerebral aneurysms and coronary arterial disease) [4,5].
As part of the project, and in close collaboration with the clinical experts in cerebral aneurysms and coronary arterial disease, a special focus will be put in ensuring that the developed techniques are usable and meaningful to clinical practice to maximise the chances of its adoption and translation to the clinical world.
At the end of this PhD project, a range of techniques will be readily available to be used in other candent areas in healthcare where there are multiscale in silico models developed (e.g. cancer, cardiovascular system, musculoskeletal system…).
[1] Damianou, A., et al. (2012). Manifold Relevance Determination. In Proceedings of ICML.
[2] Damianou, A., Lawrence, N., Ek,C. H. (2016). Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. arXiv.
[3] Settles, B. (2012). Active learning. In Synthesis Lectures on Artificial Intelligence and Machine Learning.
[4] Villa-Uriol, M.C., et al. (2011). @neurIST complex information processing toolchain for the integrated management of cerebral aneurysms.
[5] Greving, J.P. et al. (2013) Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms. The Lancet Neurology.

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

1. Villa-Uriol, MC, et al (2011). “@neurIST complex information processing toolchain for the integrated management of cerebral aneurysms.” Interface Focus, 1(3): 308-319.
2. Álvarez, MA, et al. (2012). “Kernels for Vector-Valued Functions: A Review.” Foundations and Trends® in Machine Learning, 4(3): 195-266.
3. Morris, PD, et al (2016). “Computational fluid dynamics modelling in cardiovascular medicine.” Heart, 102(1):18-28.

How good is research at University of Sheffield in Computer Science and Informatics?

FTE Category A staff submitted: 30.50

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

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