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 Mohamed Bader-El-Den and Dr Alaa Mohaseb.
The work on this project could involve:
- Machine Learning and Data Mining
- Healthcare and ICU analytics
- Consumer behaviour analysis and prediction
The Intensive Care Unit (ICU) treats the most severely ill patients in hospital who require life-sustaining treatments or extensive monitoring. In the UK, approximately 142000 patients are admitted to ICU each year. Throughout the UK, 15% of intensive care patients die while in ICU and a further 6% die before leaving the hospital. Moreover, Hospitals are subject to multiple pressures, including limited funds and healthcare resources. The intensive care unit (ICU) in particular has drawn considerable attention from the medical community due to its critically ill patients and costly resources. This attention is expected to rapidly grow of the next years due to COVID-19. The ICU patient is highly monitored using electronic equipment to measure physiological data, which provides a rich opportunity for valuable clinical intelligent data analysis using Artificial Intelligence (AI) and Machine Learning (ML).
The anticipated outcome of the patient’s treatment (e.g. survival, short-term and long-term complications etc.) plays an important role in the ICU admission decision making for both clinicians and patients. Currently, estimating the expected outcome of ICU treatment is based on general statistical results. There are a few scoring models/equations currently used in NHS ICU units (e.g. APACHE II, SAPIII and SOFA), however these models have limited benefits for individual patients and are mainly used for statistical analysis to measure ICUs overall performance. Most such models are only applicable after 24 to 48 hours which also limits their usefulness for prediction.
The project is based on the hypothesis is that it is possible to use machine learning to identify a set of patient states, from which outcome prediction algorithms may generate more accurate results than has been possible to date. The target of this project aims to expand the development of machine learning models for patients’ outcome prediction in ICU. The project to expand a recent study conducted by the supervision team . The successful candidate will be co-supervised by Dr Mohamed Bader-El-Den, the director of the Data Science and Analytics subject group at school of computing and Dr James McNicholas, Consultant in Critical Care and Anaesthetics at Queen Alexandra Hospital.
 Awad, A., Bader-El-Den, M., McNicholas, J., & Briggs, J. (2017). Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. International journal of medical informatics, 108, 185-195.
General admissions criteria
You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
Specific candidate requirements
- Programming skills using Python or relevant languages (e.g. Java, R, C++) are essential [essential].
- Background in computer science, software engineering or a related subject [essential].
- Machine Learning, Big Data and/or Data Mining experiences [desired].
- A keen interest in practical problem-solving
- Excellent interpersonal and organisational skills
How to Apply
We encourage you to contact Dr Mohamed Bader-El-Den (Mohamed.Bader-El-Den@port.ac.uk) to discuss your interest before you apply, quoting the project code below.
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:COMP5961023