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Machine Learning Methods for Behaviour Analysis in Cardiovascular Disease


Project Description

Cardiovascular diseases affect patient’s day-to-day activities by causing symptoms such as breathlessness on exertion but there is no effective way to measure this. Instead, doctors rely on asking patients questions in clinic about their symptoms from memory. Potentially life-changing decisions (such as whether to operate) are based on the answers to these questions, but the information obtained is very inaccurate, particularly as many patients struggle to recall or describe their symptoms accurately. It is likely that the application of cheap, wearable technologies such as smartphones, watches, etc. would (if properly analysed) provide the behavioural data required to better diagnose and treat cardiovascular disease.

Acquiring sensor data from phones, watches and other sensors is now straightforward, but analysis of this data remains highly challenging. Sensors extract large volumes of data which often are noisy and often difficult to understand. Manual analysis can lead to inaccuracies and incorrect diagnosis. Since manual processing is time and effort-consuming, not all data are included in the decision-making process and limits use of this data in following up the progression and response to treatment.
Although, recently the problem of human activity recognition with mobile devices has received a lot of attention, the effect of cardiovascular diseases on human behaviour have not yet been widely studied.

In this project, the student will work with Professors in Signal Processing, Engineering and Cardiovascular Medicine to develop analytic pipelines for real-world sensor data collected from patients with a range of cardiovascular diseases during ongoing clinical studies. This framework will be linked with Internet of Things technologies so that the collected data are transferred to a central server. Machine learning methods for unsupervised or semi-supervised behaviour analysis will be developed to establish novel approaches to diagnosis and treatment decisions by doctors.

Funding Notes

The Faculty of Medicine, Dentistry and Health have received one EPSRC studentship for 2020 entry from the Doctoral Training Partnership grant that is awarded to the University of Sheffield to fund PhD studentships in the EPSRC remit. This studentship will be 42 months in duration, and include home fee, stipend at UKRI rates and a research training support grant (RTSG) of £4,500.

Home/EU students must have spent the 3 years immediately preceding the start of their course in the UK to receive the full funding.

References

Eligibility:
Candidates must have a first or upper second class honors degree or significant research experience.

Enquiries:
Interested candidates should in the first instance contact Prof Tim Chico ([email protected])

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

Please clearly state the prospective main supervisor in the respective box and select 'Infection, Immunity & Cardiovascular Disease' as the department.

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