About the Project
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 Electrical and Mechanical Engineering and will be supervised by Dr Shamsul Masum, Dr Edward Smart and Nick Sangala (Consultant from Portsmouth Hospital University Trust).
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.
Project description
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 IDH 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.K., Hopgood, A.A., Lewis, R.J. and Sangala, N.C., 2024. Prediction of hypotension during haemodialysis through data analytics and machine learning. Journal of Kidney Care, 9(5), pp.215-225.
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