• Lancaster University Featured PhD Programmes
  • University of Manchester Featured PhD Programmes
  • Coventry University Featured PhD Programmes
  • University of Birmingham Featured PhD Programmes
  • University of Glasgow Featured PhD Programmes
  • Ross University School of Veterinary Medicine Featured PhD Programmes
  • FindA University Ltd Featured PhD Programmes
Ludwig-Maximilians-Universität Munich Featured PhD Programmes
University of Liverpool Featured PhD Programmes
Imperial College London Featured PhD Programmes
Coventry University Featured PhD Programmes
University of Exeter Featured PhD Programmes

Health Informatics: Funded PhD Studentship: Using Healthcare Data Analytics and Text Mining to extract valuable knowledge from SNOMED CT derived clinical narratives

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

*This scholarship is part funded by the Welsh Government’s European Social Fund (ESF) convergence programme for West Wales and the Valleys.*

Subject study:

Health informatics, natural language processing, machine learning, text analytics, public health, epidemiology

Key Information:

Healthcare systems have collected mountains of textual and numeric patient records about disease activities, hospital admissions and visits, drug prescriptions, physician notes and more. But medical research and related industries like pharmaceutical industry are faced with enormous challenges as a result of the very restrictive handling of such health data.

This PhD studentship offers an exciting opportunity of exploring and /or developing machine learning, natural language processing and text analytics techniques to extract valuable knowledge from SNOMED CT derived clinical narratives. Such knowledge will enable better care, prognosis of patients, promotion of clinical and research initiatives, fewer medical errors and lower costs, and thus a better patient life.

This project will involve industrial collaboration with Clinithink Ltd.

The successful student will have the chance of working in a very dynamic academic research environment offered by the world class UK Farr Institute of Health Informatics Research (http://www.farrinstitute.org/). We make up one part of this Institute – CIPHER (The Centre for Improvement in Population Health through E-records Research): http://www.swansea.ac.uk/medicine/research/researchthemes/patientpopulationhealthandinformatics/ehealth-and-informatics-research/thefarrinstitutecipher/

You will be supervised by:
Professor Ronan Lyons (http://www.swansea.ac.uk/staff/medicine/research/lyonsra/)
Dr Shang-Ming Zhou (http://www.swansea.ac.uk/staff/medicine/learningandteaching/zhous/)
Mr Phil Davies

The successful candidate is expected to start their PhD scholarship in July or October 2017.


Applicants should have a minimum of a 2.1 undergraduate degree and/or a Master’s degree (or equivalent qualification) in Computer Science, Computational Linguistics, Computing, Data science, Statistics, Epidemiology, Health informatics, Medical Informatics, Bioinformatics, or any other related areas.

This PhD scholarship is open to UK or EU applicants, or applicants with indefinite leave to remain in the UK.

Please visit our website for more information regarding eligibility criteria: http://www.swansea.ac.uk/postgraduate/scholarships/research/health-informatics-kess-phd-healthcare-data-analytics.php

Funding Notes

The studentship covers the full cost of UK/EU tuition fees, plus a stipend. The bursary will be limited to a maximum of £14,198 p.a. dependent upon the applicant's financial circumstances.

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here
* required field
Send a copy to me for my own records.

Your enquiry has been emailed successfully

Cookie Policy    X