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Statistical and machine learning methods for risk prediction of cardiovascular diseases from complex longitudinal data

Project Description

Project description
Risk prediction models are used in clinical decision making and are used to help patients make an informed choice about their treatment. Cardiovascular diseases are longitudinal processes, yet many risk prediction models are often based on risk determined at baseline and outcomes recorded 5 to 10 years later. Longitudinal approaches are needed to analyse the risks appropriately and to derive risk estimates. The challenges are: how to do the analyses when some data are missing, how to incorporate irregular time intervals and how to choose the best set of biomarkers for the model.

Specifically, the project will address the following research objectives:
1) Develop a variable selection method, to select the best collection of the biomarkers for the disease prediction.
2) Develop a multivariate longitudinal statistical model that will incorporate the clinical, imaging and laboratory data and derive the personalised risk predictions.
3) Risk prediction model using machine-learning methods and comparison with statistical modelling approach.

This is a three-year project from strategic research collaboration based at the new Liverpool Centre for Cardiovascular Science (LCCS) between Liverpool Heart & Chest Hospital, Liverpool John Moores University, Liverpool Health Partners, and the University of Liverpool for the advancement of cardiovascular and stroke research.

The prospective PhD student will be supervised by Dr Gabriela Czanner, Dr Ivan Olier, Professor Gregory Lip, and Professor Paulo J. Lisboa. Dr Czanner is Senior Lecturer in Statistics and Data Science. Her research interests are in statistical modelling, imaging data, complex datasets and medical applications. Dr Olier is Senior Lecturer in Data Science. His research interests are in statistical and machine learning modelling of big data, with particular focus on modelling domains and relational data. He carries out data analysis over large and extensive primary care and cardiovascular datasets, and develops machine-learning methods around them for more complex analyses. Professor Lip, the LCCS leader, is an international research leader in atrial fibrillation (AF), a heart condition that causes an irregular and often abnormally fast heart rate, and leads to a high risk of stroke and death. Professor Lisboa is the head of the LJMU’s Department of Applied Mathematics and Engineering and Technology Research Institute. His research focus is in advanced data analysis for decision support.

Academic requirements
Applicants should have a good first degree (2:1 or above) in a relevant discipline. A Master’s degree in Mathematics, Computer Science, Data Science or related discipline is desirable. The prospective PhD student will be based at Liverpool John Moores University. A programme of formal research training will be provided.

How to apply? Application process
If you are interested into this project you should email a CV, covering letter detailing your motivation and suitability for the project and contact details of two referees to Dr Gabriela Czanner ( ) by 28 February.

Funding Notes

Funding notes
This project is part of a competition funded by the Liverpool John Moores University. Closing Date 28th February 2019.

The successful candidate will gain expertise on statistical modelling, computer simulations, image analysis, data science and machine learning.

Full funding is available to UK/EU candidates only, full-time PhD study only, covering a full studentship for three years. Successful applicants should expect to commence study in September 2019.

How good is research at Liverpool John Moores University in Computer Science and Informatics?

FTE Category A staff submitted: 9.70

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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