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PhD Opportunities

Artificial Intelligence approaches for analysing patients with Obstructive Sleep Apnea

School of Electronics, Electrical Engineering and Computer Science | PHD

Applications are now CLOSED
Reference Number
Application Deadline
15 March 2023
Start Date
1 October 2023


Obstructive Sleep Apnoea (OSA) is a major sleep disorder causing by the collapses of upper airway during sleep. It is known to have relationships with many severe health problems such as diabetes, heart diseases, cancer and increased risk of mortality. It has been estimated that 1.5-10 million people are suffering from OSA in UK with higher risks of heart attack (2.3 times), hypertension (2.5 times), or heart failure (3.9 times), etc., costing NHS £28- £96 million a year. However, OSA is a heterogeneous disease with different symptoms and comorbidities for patients even though they show the same level of severity. Thus, analysing it is a very challenging task. Most of researches on OSA only focus on traditional statistical analysis methods so far. Recently, there are a few attempts applying Machine Learning methods to look for important patterns from patient data such as allocating patients into well-defined phenotypes (subgroups) based on clinical information using data clustering methods, an unsupervised branch of Machine Learning/Data Mining (ML/DM). This can help to improve the clinical management and to define personalized treatments at time of diagnosis. Some other works try to predict the OSA severity based on some clinical diagnosis information.

The aim of this interdisciplinary research project is the application and development of novel supervised (e.g., data clustering, outlier detection, pattern mining, etc.) and unsupervised machine learning/ data analysis algorithms (e.g., Deep Learning, etc.) that can be applied with longitudinal heterogeneous medical records (patient trajectories) for the purpose of disease diagnosis/prognosis, and pattern analysis over time. It is built upon several different very large datasets provided by our medical project partners.

Funding Information

*Please note that the deadline for applications from international candidates closed on 28 February*

Please note that funding may be available for this project (still to be confirmed). To be eligible for consideration for a DfE Studentship (covering tuition fees and maintenance stipend of approx. £17,668 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications. The Studentship is open to UK and ROI nationals, and to EU nationals with settled status in the UK, subject to meeting the specific DfE nationality and residency criteria. Full eligibility information can be viewed via:

Academic Requirements:
The minimum academic requirement for admission is normally an Upper Second Class Honours degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.

Project Summary

Thai Son Mai

Research Profile

Mode of Study

Full-time: 3 Years

Funding Body
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