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 Professor Jim Khan (Consultant from Portsmouth Hospital University Trust).
The work on this project will include:
- 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.
Project description
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 decisions. Additionally, the project will contribute to the development of data-driven approaches to healthcare delivery, which can improve the efficiency and effectiveness of care.
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