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Mobile Health Application to self-manage Postnatal Depression based on Artificial intelligence Intervention

   School of Computing

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  Dr Bola Omisade, Dr Alice Good , Dr Alexander Gegov  Applications accepted all year round  Self-Funded PhD Students Only

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 Computing and will be supervised by Dr Omobolanle Omisade, Dr Alice Good and Dr Alexander Gegov.

The work on this project will:

  • Explore and investigate various Artificial Intelligent (AI) methods to enhance self-management of Postnatal Depression (PND) interventions.
  • Explore various techniques that can facilitate engagement, adherence behaviour in women with PND 
  • Develop, optimise and evaluate AI-based mobile health applications to self-manage PND.

Project description

Research shows that most women suffering from PND remain untreated, do not engage with, or adhere to treatments. Many limitations of existing interventions could potentially be addressed through AI and mobile health interventions. AI, mobile health and related technologies are beginning to be applied to healthcare and can perform as well as or better than humans at key healthcare tasks. These technologies have the potential to transform many aspects of patient care, as well as diagnosis, delivering therapeutic content, engagement, adherence and self-management. 

This PhD aims to explore and investigate various artificial intelligence methods to enhance the delivery, monitoring and self-management of Postnatal Depression interventions. It will explore the various techniques that can facilitate engagement, adherence behaviour in women with PND. Furthermore, it will develop, optimise and evaluate AI-based mobile health applications to self-manage PND.

This rigorous strategy will deepen the understanding and establish an artificial intelligence method to enhance self-management of PND interventions. This outcome could be applied in research on AI for other categories of mental health. Health practitioners or mobile application developers who would like to design and develop effective AI and mobile health self-management tools could also benefit from this research. 

This is an exciting opportunity for the PhD candidate to work in a multidisciplinary research team and benefit from the rigorous approach and strategies that would be applied in this research project. It will be an opportunity to work on an innovative research project that is focused on improving and saving lives. It will also be an excellent opportunity to improve your skills and career development. 

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

You should have experience and knowledge in:

  • AI (and Machine Learning).
  • Programming applications (eg C/C++ or Java), 
  • Programming and Designing for mobile
  • User Experience Design and Human Interaction

You will be able to work independently as well as part of a team, be able to build relationships and networks. You will have proactive problem-solving skills, good time management and the ability to share, explain and interpret complex information to a range of audiences and stakeholders.

How to Apply

We encourage you to contact Dr Omobolanle Omisade () 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:COMP5841023

Funding Notes

Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK students only).
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