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Best practice on synthetic data generation and reusable code


   School of Computing

  Dr Elisavet Andrikopoulou  Tuesday, January 31, 2023  Competition Funded PhD Project (Students Worldwide)

About the Project

Applications are invited for a three year full-time (or up to six year part-time) PhD to commence in October 2023.

The PhD will be based in the School of Computing and will be supervised by Dr Elisavet Andrikopoulou.

The work on this project could involve:

  • qualitative exploration of the current scene of interoperability and clinical decision making through observation, interviews and prototyping with clinicians and quantitative analysis of outcome variables such as metrics of usability, patient outcomes and quality of decision making
  • interdisciplinary wok benefiting from our excellent networks with healthcare organisations, government agencies and professional bodies

Project description

The project aims to create purposeful synthetic healthcare dataset(s) based on realistic or real use cases. Based on the creation of the above dataset(s), the next step is to create and test new code libraries. These libraries can be realistically tested on the generated dataset, which will mimic the real hospital datasets. 

To do this, the student will need to evaluate the current scene of open source datasets and how we can use artificial intelligence and machine learning to generate data. The student would have to assess how clinical decision making is performed and evaluate the final product.

Clinical Practice Guidelines (CPG), meant to express best practices in healthcare, are commonly presented as narrative documents communicating care processes, decision making, and clinical case knowledge. These narratives lack the specificity and conciseness in their use of language to unambiguously express quality clinical recommendations. Healthcare domain-specific languages are used to describe a CPG provide a standardized, shareable, computable artefact that leaves little room for misinterpretation or ambiguity. 

Health informatics educators, developers and computing students face a huge challenge into accessing realist, structured, purposeful datasets to work on, learn and test their codes. Computable knowledge artefact development is challenging and often culminates in the development of unique single usage solutions. Computable knowledge artefacts are notoriously un-sharable and often hidden behind corporate walls. Developing libraries of structured, tested, meaningful and sharable computable knowledge artefacts and to enhance the Learning Health System, can improve the benefits of innovation and the decision making of clinicians.

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

This project would particularly suit a student with a Computer science or similar background or strong professional experience in database administration and/or artificial intelligence. Some healthcare experience would be beneficial, though specialist expertise is not required.

How to Apply

We encourage you to contact Dr Elisavet Andrikopoulou () 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 Computing 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:COMP7720423


Funding Notes

Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these may provide tuition fee discounts and/or a stipend. This project is also available to students who are self-funded or hold (or likely to hold) scholarships from other organisations or governments.

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