Competition Funded Studentship Construction, presentation and analysis of clinical e-pathways built from routinely collected patient centric data

Deadline: 30 April 2014.
Start Date: October 2014.
Supervisory Team: Dr Beatriz de la Iglesia, Email:

The Project:
We have previously defined a methodology for extracting patient centric data from multiple heterogeneous hospital information systems (HIS). The collection of data on patients with prostate cancer has been one successful example of our approach and has establised proof of principle for this proposal.

After collecting the data and generating an Operational Data Store (ODS) containing patient centric information on thousands of patients with prostate cancer, we have proposed a framework for the construction of authoritative e-pathways from such data. However, a number of challenges still remaining. First, retrieving, linking and collating patient centric data is non-trivial. We need to implement a system that will periodically collect, link and analyse new data and add it to the ODS. Second, although we can now present e-pathways for over 1,900 patients, we would like to build capability to conduct analysis such as compliance with the NICE defined prostate cancer pathway, survival of patients on different treatments or ability to match a patient’s e-pathway with other similar e-pathways to look for possible outcomes.Finally, we believe that it is possible to extend all our work on building and analysing e-pathways to other diseases or other contexts.

The proposed project will deliver on some or all of those outstanding tasks. The main objective will be to develop data analysis techniques that apply to large data sets containing clinical pathways. Some of the current techniques (e.g. the map reduce style of computation) may be relevant in the context of this project. Algorithms for clustering and classification of pathways which include time series data as well as other structured data will have to be developed. The selected student will require good programming skills as well as an understanding of database systems. Some background knowledge on machine learning, data mining and/or health informatics will be desirable.

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Entry Requirements:
A first degree (2:1) in Computer Science or related discipline.

This project is in a competition for two funded 3 year studentships within the School of Computing Sciences, one of which is funded by the University and the other by EPSRC.

Funding for the studentship from EPSRC is available to successful candidates who meet the UK Research Council eligibility criteria including the 3-year UK residency requirements. These requirements are detailed in the EPSRC eligibility guide which can be found at In most cases UK and EU nationals who have been ordinarily resident in the UK for 3 years prior to the start of the course are eligible for a full-award. Other EU nationals may qualify for a fees only award. All candidates should check to confirm their eligibility for funding.

An annual stipend of £13,726 will be available to the successful candidate.

Making Your Application:

Please apply via the University’s online application system, by clicking on the Apply button below.

NB Applications are processed as soon as they are received, so early application is encouraged.

To discuss the application process or particular projects, please contact the: Admissions Office, email: or telephone +44 (0)1603 591709. 

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