Mobile menu icon
Skip to navigation | Skip to main content | Skip to footer
Mobile menu icon Search iconSearch
Search type

Department of Computer Science


(EPSRC DTP) Applying Natural Language Processing to real-world patient data to optimise cancer care

Primary supervisor

Contact admissions office

Other projects with the same supervisor

Funding

  • Directly Funded Project (European/UK Students Only)

This research project has funding attached. Funding for this project is available to citizens of a number of European countries (including the UK). In most cases this will include all EU nationals. However full funding may not be available to all applicants and you should read the full department and project details for further information.

Project description

Decisions on patient treatment are based on evidence which is usually generated through the use of clinical trials. However, only a small fraction of patients participate in these studies, and many patient groups such as the elderly, those with multiple medical problems, and ethnic minorities, are under-represented. This means there are large sections of the population where the available evidence might not apply. Routine ???real-world??? patient data, collected about every patient as part of their normal treatment, offers an opportunity to provide evidence where clinical trial data doesn???t or will not exist. Artificial Intelligence and machine learning approaches can be used to generate evidence from real-world data but need the data to be structured to enable its processing. Similarly, prospective assessment of the impact of healthcare innovations requires patient and doctor reported clinical outcomes to be amenable to digital statistical analysis. The vision is to learn from every patient treated. Modern Electronic Healthcare Records (EHRs) can collect data in the required format. However, historical data often exists only as free-text medical notes. Furthermore, different medical groups are at different stages in the adoption of structured EHRs and can prioritise the collection of different data items. In this project, we will develop and apply Natural Language Processing (NLP) technologies to recover structured data from medical notes. We will use these data to validate and improve models to predict cancer patients??? clinical outcome, and to see if patients??? experience of their cancer treatment agrees with clinical assessments of their outcome.This project is an exciting collaboration between the Manchester Cancer Research Centre and Department of Computer Science/Alan Turing Institute, and as such will benefit from close proximity to the clinical teams at The Christie NHS Foundation Trust, the largest single site cancer centre in Europe.

https://www.mcrc.manchester.ac.uk/research/research-themes/radiotherapy/radiotherapy-big-data/

https://www.cs.manchester.ac.uk/research/expertise/natural-language-processing/

Entry Requirements:

Applications are invited from UK nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

To be considered for this project you MUST submit a formal online application form.

***For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/)***

Person specification

For information

Essential

Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.

Desirable

Applicants will be required to evidence the following skills and qualifications.

  • You will have good time management.
  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.

General

Applicants will be required to address the following.

  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Why do you believe you are suitable for doing Postgraduate Research?