University of Leeds Featured PhD Programmes
Engineering and Physical Sciences Research Council Featured PhD Programmes
University of Liverpool Featured PhD Programmes
University of Kent Featured PhD Programmes
University of Sheffield Featured PhD Programmes

BIRM-CAM: Bringing Innovative Research Methods to Clustering Analysis of Multimorbidity

About This PhD Project

Project Description

Research interests/description of main research theme:

Multimorbidity is when people suffer from more than one long-term illness. It is increasingly common as people live longer. It is important because individual illnesses have knock-on effects on others, it is more complex managing multiple than single illnesses, and multimorbid patients are heavy users of medications and health services.

To understand multimorbidity we need to know which illnesses tend to occur together and which illness combinations most affect health. To adapt health services we need to know which types of people develop multimorbidity: their age, sex, ethnicity, socio-economic status and whether they tend to live in the same households. To learn how to prevent it we need to identify lifestyle factors (physical activity, diet, smoking, alcohol) linked to multimorbidity and the measurements (laboratory test results, weight, blood pressure) that might be early signs.

Electronic health records are a good source of information on multimorbidity because they include information on the same patient over many years. They include information on illnesses, medications, hospital admissions; measurements (laboratory tests, weight, blood pressure) and lifestyle (smoking, alcohol). Previous research has studied multimorbidity using a variety of statistical methods. It finds some illnesses, such as diabetes and heart disease tend to occur together. But different statistical methods often find different groups of illnesses. We need a single, consistent approach to this type of analysis to ensure we are researching the same groups of illnesses. Previous research generally has not made best use of all the available information. For example, patients are considered either to have or not have diabetes but research did not make use of laboratory measurements (such as blood glucose) identifying some people as likely to develop diabetes. Previous research grouped illnesses according to how commonly they occur together, without giving any special significance to combinations of illnesses linked to risk of death or hospital admission. Clearly such combinations of illness are of more importance. There are more advanced analysis methods which can address these and other shortcomings.
The project will involve developing state-of-the-art techniques in statistical machine learning to tackle these critical problems in multimorbidity research. A substantial effort will be required to develop a structured machine-based intelligence approach to identify health insights from the analysis of electronic health records that account for biases and confounders that would defy off-the-shelf black-box techniques. The project would suit a candidate who wishes to develop machine intelligence techniques for substantial real-world health applications.

This ambitious work will be supported by a team of supervisors: Dr Christopher Yau (Machine Learning), Professor Tom Marshall (Applied Health) and Dr Krishnarajah Nirantharakumar (Applied Health) with internationally renowned expertise in machine learning and applied health research using electronic health data. The project will integrate with a new UK Medical Research Council funded project (Lead: Marshall) formed from a collaboration between the Universities of Birmingham and Cambridge. Our group has strong links to key national institutions such as the Alan Turing Institute (Yau) and Health Data Research UK (Nirantharakumar) that will provide the candidate with a cutting-edge environment to develop their career. The studentship will provide substantial consumable and travel funds to support training activities.

Person Specification

Applicants should have a strong background in quantitative sciences, and ideally a background in statistics and machine learning. They should have a commitment to research in the applied health sciences using machine learning and data science and hold or realistically expect to obtain at least an Upper Second Class Honours Degree in mathematics, computer science, physics or engineering.

General entry requirements can be found here

Enquiries about PhD can be directed to Christopher Yau ()


Rukat, T., Holmes, C., Titsias, M., and Yau. C. (2017) Bayesian Boolean Matrix Factorisation, International Conference on Machine Learning.
Campbell, K. R., Yau, C. (2018). Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data. Nature Communications 9 (1), 2442.
Adderley N., Nirantharakumar, K., Marshall, T. (2018). Risk of stroke and transient ischaemic attack in patients with a diagnosis of ‘resolved’ atrial fibrillation. BMJ May 9;361:k1717.
Kumarendran B, O'Reilly MW, Manolopoulos KN, Toulis KA, Gokhale KM, Sitch AJ, Wijeyaratne CN, Coomarasamy A, Arlt W, Nirantharakumar, K. (2018). Polycystic ovary syndrome, androgen excess, and the risk of non-alcoholic fatty liver disease in women: A longitudinal study based on a United Kingdom primary care database. PLoS Medicine. Mar 28;15(3):e1002542.
Turner GM, Calvert M, Feltham MG, Ryan R, Fitzmaurice D, Cheng KK, Marshall T (2016). Under-prescribing of Prevention Drugs for the Primary Prevention of Stroke and Transient Ischemic Attack in UK General Practice: Retrospective Analysis. PLoS Medicine, Nov 15;13(11):e1002169.

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

FindAPhD. Copyright 2005-2019
All rights reserved.