Process Mining in the NHS
Industry is constantly seeking to improve its processes in order to cut costs, provide better services, and improve efficiency. Amongst the many techniques currently used to achieve this objective has been process modelling. However, with the increasing pervasiveness of interconnected monitoring devices it is now possible to collect process logs and subsequently perform Process Mining on these logs in order to elicit the ‘actual’ processes that are taking place. These elicited processes could potentially be useful in performance analysis, establishing process conformance between planned and actual processes, as well as uncovering potential areas for process improvement. We have partnerships with the NHS as well as Tata Steel, and are seeking PhD candidates to carry out case studies within either of these industries. The successful candidate will work in collaboration with our partners to develop and research novel approaches and algorithms for the exploration and application of process mining techniques. Applicants should ideally have an interest in data/process mining, or any of the following: statistical methods, big data, or machine learning techniques. The research student will be equipped with a state of the art computer running data/process mining software (MATLAB,SPSS,SAS, ProM,Disco), and supplied with a 1TB database of records. Successful applicants will go through the University’s Doctoral Researcher Development Programme (DRDP), and will be members of the Health and Social Care Modelling group to assist them with collaboration and research dissemination. Students will have the opportunity to attend national and international meetings for dissemination of their research.
The Studentship consists of a fee waiver and a stipend of £16,000 per annum. Successful candidates will be expected to undertake some teaching duties.
Asaduzzaman, Md. and Chaussalet, Thierry J. (2014) Capacity planning of a perinatal network with generalised loss network model with overflow. European Journal of Operational Research, 232 (1). pp. 178-185. ISSN 0377-2217
Weigold, Thomas and Buhler, Peter and Thiyagalingam, Jeyarajan and Basukoski, Artie and Getov, Vladimir (2008) Advanced Grid programming with components: a biometric identification case study. In: Proceedings of the 32nd Annual IEEE International Computer Software and Applications Conference, 28 July - 1 August 2008, Turku, Finland: COMPSAC 2008. IEEE, Los Alamitos, USA, pp. 401-408. ISBN 9780769532622
How good is research at University of Westminster in Computer Science and Informatics?
FTE Category A staff submitted: 19.65
Research output data provided by the Research Excellence Framework (REF)
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