Funding

Competition funded (UK/EU and international students)

Project code

SENE8750124

Department

School of Energy and Electronic Engineering

Start dates

April 2024

Application deadline

19 January 2024

Applications are invited for a fully-funded three year PhD to commence in April 2024. 

The PhD will be based in the Schools of Energy and Electronic Engineering and Mechanical and Design Engineering, and will be supervised by Dr Shamsul Masum, Prof. Adrian Hopgood and Dr Nick Sangala.

Successful applicants will receive a bursary to cover tuition fees for three years and a stipend in line with the UKRI rate (£18,622 for 2023/24). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables. 

 

The work on this project will:

  • Aim to identify trends in patients with IDH using routinely collected hospital data.
  • Use electronic health records (EHRs) and other sources of routinely collected hospital data to identify IDH patterns in patient demographics, clinical characteristics, and outcomes.
  • Focus on identifying trends in IDH prevalence, diagnosis, and management, as well as identifying risk factors for IDH progression and adverse outcomes.
  • Use advanced analytics techniques, such as machine learning and predictive modelling, to identify patterns and associations in the data.
  • Involve collaboration with healthcare providers (Portsmouth Hospital University Trust), researchers (both Hospital and University), and other stakeholders to ensure that the findings are relevant and actionable.
  • Have several phases, including data collection and cleaning, data analysis and modelling, and interpretation of results.
  • Expected outcomes include a better understanding of IDH trends, improved identification of patients at risk of IDH progression, and the development of targeted interventions to improve patient outcomes.
  • Contribute to the development of data-driven approaches to healthcare delivery, which can improve the quality and efficiency of care for patients with the risk of IDH.

 

Many individuals with end-stage renal disease (ESRD) receive haemodialysis at home or in a centre to sustain life. Intradialytic hypotension (IDH) – sudden and significant reductions in blood pressure during dialysis - is a common and challenging complication. Strategies to prevent IHD rely on predicting when IDH is likely to occur. Informed assessment of routine observations by a clinician before a dialysis session is currently the only way to do this, but it is a method that can be time-consuming and achieves a variable degree of success. An IDH event can lead to patient discomfort, dialysis interruption, hospitalization, increased morbidity, mortality, cerebral ischemia, vascular access thrombosis, and cardiovascular events. Besides these medical consequences, IDH also leads to an increase in service costs.

An accurate prediction of IDH and a practical tool for flagging the need for an intervention to avoid IDH using AI can improve collaborative decision-making, enhance patient care, save costs, and reduce healthcare professionals' burden. The project will demonstrate the potential of artificial intelligence, machine learning, and data analytics as a practical tool for flagging the need for an intervention to avoid IDH. For the first time, novel ML algorithms will identify significant predictor variables and exploit them, and innovative knowledge-based AI will convert findings into actionable recommendations. This project has the advantage of using routinely gathered data from a real-world population of dialysis patients as a whole. The data have already been gathered in collaboration with Portsmouth Hospitals University NHS Trust. The supervision team has carried out an initial study that has shown some predictive ability [1]. The project will have several phases, including data processing and cleaning, data analysis and modelling, and interpretation of results. The project will also involve collaboration with healthcare providers, researchers, and other stakeholders to ensure that the findings are relevant and actionable.

[1] Masum, S., Hopgood, A.A., Sangala, N., and Lewis, R., Machine Learning for Intradialytic Hypotension Prediction in Haemodialysis Patients, Kidney International Reports 7(2) pp S274. Paper POS-640 (2022)

 

Entry requirements

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) 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.

Data science, Data Analytics, Artificial Intelligence, and Machine Learning.

 

 

 

 

 

How to apply

We’d encourage you to contact Dr Shamsul Masum (shamsul.masum@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form. 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.

If you want to be considered for this funded PhD opportunity you must quote project code SENE8750124 when applying.