Health and Wellbeing Research Cluster

In collaboration with colleagues from the University of Derby, regional health professionals and industries, we develop techniques and algorithms to support the health and biomedical sectors in tackling real-world problems. Our expertise includes the use of knowledge graphs, ontologies and word embeddings to represent health and medical knowledge to be exploited and used by health systems to increase interoperability and provide a better understanding and exploitation. We use machine and deep learning models to predict diseases, their spread and progress. Medical imaging techniques are used to detect the early presence of tumours and abnormalities using different types of imaging modalities.

Our aims

There is wide expertise in health and wellbeing at the University of Derby, starting from the Data Science Research Centre where there are over ten researchers that specialise in health informatics and bioinformatics. They cover areas such as medical image analysis and classification, medical knowledge systems with the use of ontologies and knowledge graphs and machine learning and data science for the analysis of clinical and omics data. Research in health and wellbeing is further supported by two Colleges. The first is the College of Science and Engineering in the School of Human Sciences. The second College is Health, Psychology and Social Care and the three new research centres under the Biomedical and Clinical Sciences research theme. The university is well-connected with local health authorities, hospitals, and clinicians. 

Health Information and Knowledge Systems

The health industry is a data-intensive business. Collecting, organising and linking data plays an important role before it is analysed and used for decision-making. We use various techniques such as knowledge graphs and ontologies to understand data, remove ambiguities and link various datasets for use with our partners in public health and voluntary and community organisations. 

Biomedical informatics

We are working with our partners and stakeholders in the health sector to develop tools and models to analyse and visualise health data. Bioinformatics helps in providing integrated data that can increase knowledge and understanding across medical disciplines to provide clinical insights and discover diseases and associated treatments. Biomedical informatics helps new ways of new lines of scientific and medical inquiry from the data analysis. 

Digital Health and Wellbeing

Developing digital solutions similar to those used during the Covid-19 pandemic will address the current pressures experienced by hospitals and general practices. This resulted in long waiting lists and the inability of patients to see their GPs. Keeping the general population healthy and providing alternative support and care outside hospitals and general practices will also contribute to reducing the pressure. We are developing digital platforms and solutions to prevent people from needing to go into hospitals or visit their GPs whenever possible without compromising the quality of care and support by working with health organisations. 

Research Cluster Team

Our research

Early Career Academic Jack Sutton, who with Dr Peter Scriven, Divisional Director at Chesterfield Royal Primary Care, is developing a statistical model using regression analysis. This has been applied to health data related to mortality, crime, property, and age matrix.

Researchers within the research cluster are also using the semantic networks to model and represent health data. This project involves international collaboration. To find out more about this research project, contact Wajahat Ali Khan and Maqbool Hussain for more details.

Join us

If you are interested in joining this research centre, want to find out more or are interested in applying for a PhD in this area, please contact Dr Maria Papadaki (


  • OO Akinsanya, M Papadaki, L Sun (2020). Towards a maturity model for health-care cloud security (M2HCS). Information & Computer Security 28 (3), 321-345. 
  • Hassan Zada, M., Yuan, B, Khan, W., Anjum, A., Reiff-Marganiec, S. and Saleem, R. 2022. A unified graph model based on molecular data binning for disease subtyping. Journal of Biomedical Informatics. pp. 1-24.  
  • Afzal, Muhammad, Malik, Khalid M., Ali, Taqdir, Ali Khan, Wajahat, Irfan, Muhammad, Jamshrf, Arif, Lee, Sungyoung and Hussain, Maqbool 2020. Acquiring Guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method. Computer Methods and Programs in Biomedicine.  
  • Syed Imran Ali, Su Woong Jung, Hafiz Syed Muhammad Bilal, Sang-Ho Lee, Jamil Hussain, Muhammad Afzal, Maqbool Hussain, Taqdir Ali, Taechoong Chung, Sungyoung Lee (2021). Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment. International Journal of Environmental Research and Public Health 19 (1), 226. 
  • M Afzal, M Hussain, J Hussain, J Bang, S Lee (2021). COVID-19 Knowledge Resource Categorization and Tracking: Conceptual Framework Study.  Journal of Medical Internet Research, 23 (6), e29730. 
  • Yu, H. and Reiff-Marganiec, S. 2022. Learning Disease Causality Knowledge from Web of Health Data. International journal on semantic web and information systems. 18 (1), pp. 1-19.   
  • Yu, H. 2020. Experimental Disease Prediction Research on Combining Natural Language Processing and Machine Learning. IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). IEEE Xplore. 
  • A Makkar, KC Santosh (2023). SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays. International Journal of Machine Learning and Cybernetics, 1-12. 
  • Afia Farjana, Aaisha Makkar (2023). Federated Learning for Lung Sound Analysis. Recent Trends in Image Processing and Pattern Recognition: 5th International Conference, RTIP2R 2022, Kingsville, TX, USA, December 1-2, 2022, Revised Selected Papers, pp. 120-134. 
  • Q Hanley, J Sutton, G Shahtahmassebi, H Ribeiro (2022). Population density and spreading of COVID-19 in England and Wales. PLoS ONE 17 (3). 
  • J Sutton, G Shahtahmassebi, HV Ribeiro, QS Hanley (2020). Rural–urban scaling of age, mortality, crime and property reveals a loss of expected self-similar behaviour. Scientific reports 10 (1), 1686.