Funding

Self-funded

Project code

SEM10330526

Start dates

October, February and April

Application deadline

Applications accepted all year round

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 Professor Jim Khan (Consultant from Portsmouth Hospital University Trust).

The work on this project will:

  • Data analytics and artificial intelligence to predict the length of stay, readmission, and mortality after colorectal cancer surgery.

  • The use of a variety of data sources, including electronic health records (EHRs), administrative data, and other clinical data, to identify patterns and associations between patient characteristics and outcomes.

  • Several phases, including data collection and cleaning, statistical and data analysis, feature engineering, modelling, validation, trial, and testing.

  • Machine learning algorithms, such as logistic regression, random forest, and neural networks, to develop predictive models for each outcome of interest.

  • Identification of patient characteristics that are associated with increased risk of adverse outcomes, such as age, comorbidities, surgical approach, and surgical volume.

  • Improved patient outcomes, reduced costs, and improved quality of care.

  • Development of data-driven approaches to healthcare delivery, which can improve the efficiency and effectiveness of care colorectal cancer.

  • A significant contribution to the field of colorectal cancer research and patient care.

  • 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.

Colorectal cancer (CRC) is the third most common cancer by incidence, with over 1.8 million new cases in 2018. The economic impact of colorectal cancer on healthcare systems is immense. With limited resources and a finite surgical bed capacity in many hospitals, it is extremely important to know the expected Length of Stay (LOS), readmission rate, and mortality after elective CRC surgery. An accurate prediction of LOS, readmission, and mortality would help healthcare professionals with planning, decision-making, and building strategies. This will eventually lead to improved patient care, save potential costs, and prevent readmission and mortality after discharge.

The Hypothesis is that AI and Data analytics techniques can be used to effectively identify potential long stay, readmission, and mortality patients early enough following colorectal surgery for the knowledge to be useful in shortening their stay and better planning and management.

The expected outcome of the project is an AI system that can identify significant predictor variables, predict patient outcomes, and make both clinical and management. Additionally, the project will contribute to the development of data-driven approaches to healthcare delivery, which can improve the efficiency and effectiveness of care. While various AI approaches have been used to develop prediction models, their clinical implementation and trial have been limited. Criticisms of these models include their reliance on reliable datasets, validation issues, and the opaque nature of 'black-box' models with limited understanding or explanation capability. This project seeks to address these limitations, particularly the lack of external validation. Using opaque prediction models can lead to reduced trust, but incorporating explainable AI models can help to build trust. The supervision team has carried out an initial study that has shown some predictive ability [1], but we wish to exploit a bigger dataset, investigate other data types, and add knowledge-based AI.

 

[1] Masum, S., Hopgood, A.A., Stefan, S., Flashman, K., and Khan, J.S., "Data analytics and artificial intelligence in predicting length of stay, readmission, and mortality: a population-based study of surgical management of colorectal cancer", Discover Oncology 13:11 (2022).

Fees and 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 UK Government Doctoral Loan (UK and EU students only).

 

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.

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 (Computer Science, Data science, Data analytics, Artificial intelligence, Machine Learning) or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. To make the requirements more open and inclusive, applicants who have completed a substantial project, dissertation, or professional work in a related technical area may also be considered, even if their formal degree is not directly aligned with the fields listed above.  English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

 

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, please follow the 'Apply now' link on the Electronic Engineering 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.

If you want to be considered for this self-funded PhD opportunity you must quote project code SEM10470526 when applying.