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Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Elisavet Andrikopoulou and Ruth De Vos.

Context

Breathing pattern disorder (BPD) is defined as a chronic change in the pattern of pattern in the absence of, or in excess of organic respiratory disease (Boulding 2016). This disruption of the normal and controlled rate, depth and rhythm of breathing results in dyspnoea and a range of non-respiratory symptoms. BPD is increasingly being recognised as a significant driver of breathlessness, particularly in those with known respiratory conditions and is present in at least one third of patients with asthma (Thomas 2005). Despite this prevalence, the pathophysiology behind BPD is currently poorly understood as it is a complex and heterogeneous condition which lacks clear definition or gold standard diagnostic criteria. Diagnosis of a BPD is currently made using questionnaires which are often inconclusive, non-specific, or indeed not validated, alongside an assessment by a respiratory physiotherapist with specialist training in this condition. This is not only time consuming but runs the risk of lack of standardization and individual interpretation. improving the understanding of the mechanisms behind BPD would lead to the optimisation of patient selection for treatment and refinement of any intervention.

At present, there are few objective methods of diagnosing BPD. A deep learning system that is able to both diagnose and assess the effectiveness of treatment intervention which is easily accessible, portable, cost effective and easy to use would prove invaluable in streamlining the diagnostic process, and provide invaluable feedback to patients undergoing treatment of their BPD.

Computable knowledge unleashes the potential of information technology to generate and deliver relevant health advice to individuals and organizations with great speed on a worldwide scale. A computable knowledge artifact created for BPD diagnosis would also derive from the deep learning system. 

The supervisory team has excellent connections with NICE and the ´óÏó´«Ã½ Hospital NHS Trust and one of the members of the supervisory team is an clinical asthma nurse.

Funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the (conditions apply).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

How to apply

We’d encourage you to contact Dr Elisavet Andrikopoulou (elisavet.andrikopoulou@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Health informatics PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.  Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code: COMP6391025