School of Electronics, Electrical Engineering and Computer Science

Analysing New Onset Diabetes After Transplantation using Multivariate Models

Internships Summer 2017/18

Proposed Project Title:

  • Analysing New Onset Diabetes After Transplantation using Multivariate Models

Principal Supervisor(s):
  • Cassio P. de Campos

Project Description:

We will study the problem of predicting new onset diabetes after transplantation (NODAT). We will work in collaboration with Dr. A. J. McKnight of QUB Public Health, who will provide the dataset of patients to be analysed.

We will develop a software for pre-processing and perform curation of the data set, as well as translating it into formats commonly used by machine learning software tools, in order to explore a variety of multivariate prediction approaches and to compare their accuracy. We will build an interpretable model that can be used by practitioners to understand possible causes of NODAT.

The project might be adapted/changed according to the discussions with Dr. McKnight in order to achieve results of maximum impact.

  • Curation of a QUB’s NODAT dataset.
  • Study of multivariate models and identification of the most suitable for predicting NODAT.
  • New software package implemented in Java, C++ and R for analysing the NODAT dataset and possibly others with similar characteristics.
  • Discovery of clinical and/or genomic covariates that can be used to predict NODAT.
  • Writing of a full technical report with the discoveries.

Academic Requirements:

QUB undergraduate student with 1st class marks in most modules and demonstrable mathematical and programming skills.

Previous knowledge in the area of data analysis and artificial intelligence is preferable. Previous knowledge of LaTeX, Java and R languages is preferable.

General Information:

Each internship will last between 6-8 weeks and will pay a weekly stipend of £200.

Accommodation and travel costs are not provided under this scheme.

Start date: 5/June/2017

Duration: 8 weeks

Location: QUB CSB

Further information available at:

Contact details:

Supervisor Name: Cassio P. de Campos

Queens University of Belfast
School of EEECS,
Computer Science Building,
18 Malone Road,

Tel: +44 (0)28 9097 6795

For further information on Research Area click on link below: