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Acquisition and analysis of real-world sensing data to aid prediction, diagnosis and monitoring of cardiovascular disease

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  • Full or part time
    Dr T Chico
    Prof L Mihaylova
    Prof F Ciravegna
  • 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

Overview:
This 3.5 year studentship is part of the Healthy Lifespan Institute (HLI) at The University of Sheffield. The HLI is dedicated to the understanding and prevention of multimorbidity (the presence of two or more chronic health conditions that create disability and reduce quality of life). We are taking a unique multidisciplinary approach that spans the biological and social sciences and strive to create new practices, policies and products that target multiple conditions and help people live longer, healthier and more independent lives.

Students within the Healthy Lifespan Institute are valued and active members of the Institute and vital in contributing to our aims and helping to effect real change. You will be part of a wider multidisciplinary network of PhD students and will have the chance to influence and lead Institute activity, seminars and events, and meeting leaders in the field.

Research Project:
A major obstacle to prevention, diagnosis and treatment of chronic cardiovascular diseases is the lack of medically relevant data obtained in daily life . The increasing availability of cheap, scalable technologies to collect long-term, real-world behavioural, physiological and environmental data from patients during daily life, when combined with computer modelling, artificial intelligence, and machine learning provides an opportunity to radically improve patient care. This requires collaboration between physicians and experts in acquisition and analysis of complex datasets.

This project applies the expertise of Professor Ciravegna in real-world activity tracking (used by >900,000 users as part of Public Health England’s Active project) to further develop monitoring technologies (smartphones, smartwatches, home sensors) to acquire long term 24/7 data on indoor and outdoor physical activity, cardiac physiology, environment (weather, location, temperature) from patients recruited from Professor Chico’s cardiology clinics with a range of cardiovascular diseases.

In this project, the student will work with Professors Ciravegna and Mihaylova to develop analytic pipelines for real world sensor data collected from patients with a range of cardiovascular diseases during ongoing clinical studies. Machine learning methods for unsupervised or semi-supervised behaviour analysis will be developed to establish novel approaches to monitoring, diagnosis and treatment decisions by doctors.


Project Aims and Objectives:
The student will work on the data collected by software developed by Professor Ciravegna and under release to patients of Prof. Chico. The system will be used by hundreds of patients over the next two years. The system uses a mobile app, a set of bluetooth beacons and a smart watch to monitor patients 24/7 while indoors and outdoors.
The student will analyse data collected by the existing system and identify signatures of cardiovascular diseases. S/he will also define a data analytics platform for doctors to analyse patients’ data at an individual and population level. The data analytics platform will support cardiologists in reaching a diagnosis based on the collected, cleaned and modelled data.
Key to the project is development of algorithms that maximise sensing quality while minimising battery power consumption to collect high quality continuous data over long periods; data quality is a major requirement for maximising classification accuracy over long term collection, as well as usability of the collection technology.
The approach will be based on (i) reinforcement learning to optimise sensing strategies and battery impact, (ii) largely unsupervised and semi-supervised machine learning classification algorithms to identify signatures of disease to support diagnosis; transfer learning will be used to create strategies tailored to individual users’ behaviour while learning across users and (iii) adaptive data and visualisation analytics methods.
The outcome will be a complete analytic pipeline to be tested on datasets and from patients throughout the 3.5 years of the project.

Supervision and Mentorship:
The Healthy Lifespan Institute seeks to create highly interdisciplinary collaborations across the different faculties of the University of Sheffield and with external partners. This studentship will be jointly supervised by senior academics in the Faculty of Medicine, Dentistry & Health and the Faculty of Engineering. There will be additional mentorship from Prof. Ian Craddock, Director of SPHERE, a £20M EPSRC-funded project focused on home-monitoring of activity and and an expert in pervasive sensing and analysis.

Supervisors: Prof. Tim Chico (Department of Inflection, Immunity and Cardiovascular Disease, Faculty of Medicine, Dentistry & Health), Prof. Lyudmila Mihaylova (Automatic Control Systems Engineering, Faculty of Engineering) & Prof. Fabio Ciravegna (Department of Computer Science, Faculty of Engineering).

External Mentors: Prof. Ian Craddock (Director, EPSRC Centre for Doctoral Training in Digital Health & Care, University of Bristol)

Funding Notes

Funding details and salary/stipend rate:
Each studentship will be supported for 3.5 years with the student handing in their thesis at the end of this funding period.

Students will be provided with:
A full award paying fees and maintenance at the standard Research Council rates (stipend £15,285 & fees £4,407 in 2020-21).
Research Training Support Grant of £1,500 per year

References

Entry Requirements:
The ideal candidate will have a first class degree in computer science or related disciplines with excellent knowledge of mobile programming (Android), algorithm development, and machine learning methods. Knowledge of good practices in software development is a must. Existing knowledge in data analytics methods and tools (e.g. Jupiter Notebooks, R) will be a plus.
Applicants must be home/EU based students and ensure they adhere to the EPSRC terms of eligibility.

Proposed start date: 1st October 2020

How to apply:
Please complete a University Postgraduate Research Application form available here: www.shef.ac.uk/postgraduate/research/apply


Please clearly state the title of the studentship, the prospective main supervisor and select Department of Inflection, Immunity and Cardiovascular Disease as the department.



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