About the Project
General Practices have accumulated significant amounts of data from their registered patient population. This data is collated at an individual practice within the repository of locally hosted clinical information systems (CiS). Data mining of information systems can be undertaken for a variety of purposes, including identification and monitoring of consultations, clinical management of patients, audit and quality assurance. Through a combination of discovery and predictive data mining techniques, subset of current GP workload can be identified having disease types having certain read-codes which can be safely shifted to other clinicians. With the developing sophistication of CiS, there is an opportunity to use practice data differently and effectively.
In a recent project led by Corbridge Medical Group in collaboration with researchers from Northumbria University, processes and procedures have been developed that enables use of CiS to identify consultation behaviour and the associated workload. In this practice, it was identified that 6% of registered patients use 24% of all GP face-to-face, telephone and home visit consultations (based on the analysis of past consultation data). These procedures have been tested in two other practices with similar results (5-7% of the registered population use 24-27% of GP face-to-face, telephone and home visit consultations). Learning from the previous work can be applied in this project with the focus being workload shifting rather than frequently attending patients.
Gateshead’s GP Federation comprises 30 general practices (a population of 204,600 patients) that has a federation-wide EMIS-enterprise license and established data sharing agreements relating to patient information. This is a unique data source within the context of the NHS, however approaches to data mining and data analysis for this complex dataset have not been developed. This project aims to holistically explore this information source as ‘Big data’ through machine learning and Big Data Analytics, which would have significant outcomes for commissioning and delivery of primary care.
This project is supervised by Dr Akhtar Ali.
Please note eligibility requirement:
• Academic excellence of the proposed student in the field of computer science, data analytics or data science having at least 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]) or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score (6.5 or above), if required.
• Ideally having experience of conducting research work in the relevant domain (computer science, data analytics or data science or mathematical sciences).
For further details of how to apply, entry requirements and the application form, see
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF19/EE/CIS/ALI) will not be considered.
Start Date: 1 March 2020 or 1 October 2020
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.
DOI: https://doi.org/10.3399/bjgp17X691529. http://bjgp.org/content/67/660/322
Abdelsalam M. Maatuk ; M. Akhtar Ali ; Raja A. Moftah ; Mohammed R. Elkobaisi (2016) Performance evaluation of an RDB and an ORDB: A comparative study using the BUCKY benchmark.International Conference on Engineering and MIS (ICEMIS) http://ieeexplore.ieee.org/document/7745312/?reload=true
Negin Keivani and Abdelsalam M. Maatuk and Shadi Aljawarneh and Muhammad Akhtar Ali (2015) Towards the Maturity of Object-Relational Database Technology: Promises and Reality. IJTD Vol 6(4), pages 1-9. http://dx.doi.org/10.4018/IJTD.2015100101
Abdelsalam Maatuk., M. Ali A., Aljawarneh S. (2015) Translating Relational Database Schemas into Object-based Schemas: University Case Study. Recent Patents on Computer Science. Volume 8, No 2, pp. 122-131
Wang Y., Angelova M., Ali A. (2013) Fuzzy clustering of time series gene expression data with cubic-spline. Journal of Biosciences and Medicines, 1, 16-21. doi:10.4236/jbm.2013.13004.
Abdalla Ahmed, Abdelmuttlib Ibrahim, Ab Hamid, Siti Hafizah, Gani, Abdullah, Khan, Suleman and Khan, Muhammad Khurram (2019) Trust and reputation for Internet of Things: Fundamentals, taxonomy, and open Research Challenges. Journal of Network and Computer Applications, 145. p. 102409. ISSN 1084-8045
Shahjehan, Waleed, Riaz, Asad, Khan, Imran, Sadiq, Ali Safaa, Khan, Suleman and Khan, Muhammad Khurram (2019) Bat algorithm–based beamforming for mmWave massive MIMO systems. International Journal of Communication Systems. ISSN 1074-5351 (In Press)
Rafique, Hina, Shah, Munam Ali, Islam, Saif Ul, Maqsood, Tahir, Khan, Suleman and Maple, Carsten (2019) A Novel Bio-Inspired Hybrid Algorithm (NBIHA) for Efficient Resource Management in Fog Computing. IEEE Access, 7. pp. 115760-115773. ISSN 2169-3536
Younus, Muhammad Usman, Islam, Saif ul, Ali, Ihsan, Khan, Suleman and Khan, Muhammad Khurram (2019) A survey on software defined networking enabled smart buildings: Architecture, challenges and use cases. Journal of Network and Computer Applications, 137. pp. 62-77. ISSN 1084-8045
Maghdid, Safar A., Maghdid, Halgurd S., HmaSalah, Sherko R., Ghafoor, Kayhan Z., Sadiq, Ali Safaa and Khan, Suleman (2019) Indoor human tracking mechanism using integrated onboard smartphones Wi-Fi device and inertial sensors. Telecommunication Systems, 71 (3). pp. 447-458. ISSN 1018-4864
Ahsan, M. A. Manazir, Ali, Ihsan, Imran, Muhammad, Idris, Mohd. Yamani Idna, Khan, Suleman and Khan, Anwar (2019) A Fog-centric Secure Cloud Storage Scheme. IEEE Transactions on Sustainable Computing. ISSN 2377-3790
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