Machine Learning with Ontologies for Supporting the Management of Mental Health Problems and Disorders [Self Funded Students Only]

   Cardiff School of Computer Science & Informatics

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  Dr Alia Abdelmoty  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

According to Mind [1], 1 in 4 people will experience a mental health problem of some kind each year and 1 in 6 will report experiencing a common mental health problem (like anxiety and depression) in any given week in England. Identification, communication and integration of patients’ data can help clinicians and other medical professionals support patients and ultimately provide effective service and reduce the cost in the health care system.

A great deal of biomedical data are being created and accumulated, in labs and clinics, but also, by patients themselves on Social Media. Recently, machine learning algorithms are being used widely to detect, extract, classify and encode knowledge from big biomedical data [2].  Ontologies as conceptualisation of domain knowledge are also being used extensively in the biomedical domain to integrate and effectively utilise this data (there are more than 800 ontologies on BioPortal [3]). 

Combining ontologies and machine learning in this domain is likely to bring many benefits [4], namely: using the reasoning power of the ontology models to encapsulate and optimise knowledge representation in the domain, using ontologies for semantic similarity computation and data integration, as well as ontology embeddings to improve the learning models.

This project will build on the expertise gathered in previous, ongoing project carried out in the School on the subject of ontology-driven learning for a specific mental disorder and will extend the work into building ontologies and learning models for general mental disorders.

The project’s aim is to provide tools for aligning and integrating already established standard ontologies, such as SNOMED-CT and the Mental Disease Ontology [5]. Ontology enrichment methods will be developed using formal and informal resources on the Web. The aim is encoding language used by patients, in addition to that used by medical experts. The developed ontologies will then be used to demonstrate use cases on ontology-guided machine learning, e.g., automatic annotation of language used on Social medical forums.  The team is working closely with Cardiff School of Medicine who provide valuable domain expertise for evaluating the developed methods and resources.

Keywords: Medical Ontology Development and Enrichment, Large Language Models, Machine Learning in Biomedical Domain

Contact for more information: Dr Alia Abdelmoty - [Email Address Removed]

Academic criteria:  

A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject.  Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas. 

Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component. 

This application is open to students worldwide. 

How to apply

Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below 

This project is accepting applications all year round, for self-funded candidates via  

In order to be considered candidates must submit the following information:  

  • Supporting statement  
  • CV  
  • In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD 
  • Qualification certificates and Transcripts 
  • Proof of Funding. For example, a letter of intent from your sponsor or confirmation of self-funded status (In the funding field of your application, insert Self-Funded) 
  • References x 2  
  • Proof of English language (if applicable) 

If you have any questions or need more information, please contact [Email Address Removed] 

Computer Science (8)

Funding Notes

This project is offered for self-funded students only, or those with their own sponsorship or scholarship award.


2. Seonwoo M, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform 2016;18(5):851–869.
4. H. Dhayne, R. Haque, R. Kilany, Y. Taher, In search of big medical data integration
solutions-a comprehensive survey, IEEE Access 7 (2019) 91265–91290.
5. M. Ivanović, Z. Budimac, An overview of ontologies and data resources in medical domains, Expert Systems with Applications 41 (2014) 5158–5166.

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