Using Machine Learning to Predict Cardiovascular Disease for use in a Decision Model
Applications are now CLOSED
There are effective drugs for preventing cardiovascular disease (CVD) for individuals who are at “high risk” of a cardiovascular event. Many individuals at “low risk” prefer to judge for themselves the balance of risks, costs and benefits from taking a medication potentially for life. Machine learning techniques can be used to extract further information to aid prediction of diseases and help classify individuals into more accurate risk groups  and should be explored as advanced tools for decision-making . Decision models can help stakeholders such as patients and clinicians by estimating the associated costs and outcomes of taking a drug compared to the standard of care. Discrete Conditional Survival (DC-S) models are a family of models that can represent a skewed survival distribution for different classes of individuals. A set of related variables are used to determine the clustering or grouping of individuals into distinct classes. Previous research has used various conditional components such as decision trees, Naïve Bayes and Bayesian classifiers to classify individuals into discrete classes and represent their survival [3,4]. A number of different survival distributions can be considered and can be represented as a Markov process. Decision models often contain a Markov component that are used to describe the survival time of individuals. A Markov model can be used to describe the process where the risk of an event is continuous over time and the event can happen multiple times . Markov models are useful in healthcare for modelling the progression of a disease of an individual and have been increasingly used within decision models partly due to recent advances in computing which permits higher processing capacities.
This PhD will focus on using different machine learning techniques to predict CVD by classifying individuals into different risk groups and incorporating this into the Markov component of a decision model.
Quantitative training such as mathematics or statistics is required for this project. An interest in the area of medical statistics or data analytics and experience of either (computational and application based) will be considered an advantage.
 Angermueller, Parnamaa, Parts, Stegle, Deep learning for computational biology. Molecular systems biology, Jul 2016.
 Chirikov, Marston, et al., Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps Journal of Health Economics and Outcomes Research, Apr 2020.
 Marshall, Burns, Discrete Conditional Survival Models for trolley waiting times in Accident and Emergency. IEEE Workshop on Health Care Management, Feb 2010.
 Marshall, Payne et al., Modelling the development of late onset sepsis and length of stay using discrete conditional survival models with a classification tree component. 25th IEEE International Symposium on Computer-Based Medical Systems, June 2012.
 Sonnenberg FA, Beck JR, Markov models in medical decision making: a practical guide, Medical decision making, Dec 1993.
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Dr Felicity Lamrock
Professor Adele Marshall
Professor Frank Kee
The Mathematical Research Centre conducts world-class research in the following areas: Algebra, Analysis, Operator Algebras, Algebraic Topology, Topological Data Analysis, PDEs, Survival Analysis, Bayesian Networks, Data Analytics and Operational Research. It maintains vibrant international links with a large number of researchers around the globe and regularly hosts international conferences and research visitors.
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People teaching you
Dr David Barnes
Postgraduate Advisor - Mathematical Sciences Research Centre
Dr Salissou Moutari
Director of Research - Mathematical Sciences Research Centre
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|Northern Ireland (NI) 1||£4,500|
|Republic of Ireland (ROI) 2||£4,500|
|England, Scotland or Wales (GB) 1||£4,500|
|EU Other 3||£17,460|
1 EU citizens in the EU Settlement Scheme, with settled or pre-settled status, are expected to be charged the NI or GB tuition fee based on where they are ordinarily resident, however this is provisional and subject to the publication of the Northern Ireland Assembly Student Fees Regulations. Students who are ROI nationals resident in GB are expected to be charged the GB fee, however this is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.
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