Unravelling Atrial Fibrillation Complexity: Advanced Statistics and Machine Learning Integration of Electrophysiology Clinical Data and Big Data Sources, [Artificial Intelligence, Biostatistics, Machine Learning, Digital Healthcare

Location:

Department of Computer Science, Streatham Campus, Exeter

and

Liverpool Centre for Cardiovascular Science, William Henry Duncan Building, Liverpool

The University of Exeter’s Department of Computer Science is inviting applications for a PhD studentship funded by Liverpool Centre for Cardiovascular Science (LCCS) and the Faculty of Environment, Science and Economy to commence on January, 2025 or as soon as possible thereafter. For eligible students the studentship will cover home or international tuition fees plus an annual tax-free stipend of at least £19,237 for 3 years full-time study. The student would be based in the Department of Computer Science in the Faculty of Environment, Science and Economy at the Streatham Campus in Exeter and expected to spend majority time (approximately 9 months per year) in Liverpool Centre for Cardiovascular Science, William Henry Duncan Building, University of Liverpool.

Project Description:

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant morbidity and mortality [1]. However, its clinical presentation and response to treatment vary widely among individuals, highlighting the need for a deeper understanding of AF heterogeneity [2]. This interdisciplinary project aims to elucidate the complex landscape of AF through the integration of advanced statistics and machine learning techniques. Leveraging electrophysiology (EP) clinical data from the Liverpool Centre for Cardiovascular Science, Liverpool Heart and Chest Hospital NHS Foundation Trust and “big data” sources like TriNetX and UK Biobank, the study will apply innovative analytical frameworks to unravel distinct AF subtypes and identify underlying disease mechanisms. By comprehensively characterizing AF phenotypes and patient subgroups, the research seeks to inform personalized approaches to AF management and treatment, ultimately improving patient outcomes and advancing precision medicine in cardiology. Extensive evaluation will be conducted utilising a local large-scale retrospective dataset encompassing EP recording and corresponding clinical indices sourced from the Liverpool Centre for Cardiovascular Science. This multi-centre dataset will be a robust benchmark for assessing the model’s performance and resilience across diverse clinical contexts.

This project is in partnership with Liverpool Centre for Cardiovascular Science (LCCS). The successful PhD candidate will benefit from working with a multidisciplinary team in which there exists extensive experience in the areas of machine learning, biostatistics, and medicine: Dr Yanda Meng (Machine Learning), Prof Mark Kelson (Biostatistics), Prof Yalin Zheng (AI in Healthcare), Prof Dhiraj Gupta (Cardiovascular Medicine), Prof Gregory Lip (Cardiovascular Medicine).

If you have any specific questions regarding this studentship, please contact Dr Yanda Meng at y.m.meng@exeter.ac.uk

The studentship will be awarded on the basis of merit for 3 years of full-time study to commence on 23 January 2025.

The collaboration involves a project partner who is providing funding and other material support to the project, this means there are special terms that apply to the project, these will be discussed with Candidates at Interview and fully set out in the offer letter. Full details will be confirmed at offer stage.

International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.

For further information and to apply please click here