We have an intake of 6 students to the programme each year. Browse the tabs to find out more about the current students and their projects.
The current cohort started their PhD projects in September 2021. See below project details and click on the links for their profiles.
Supervisors - Prof Conrad Bessant, Prof Pedro Cutillas
"Using Artificial Intelligence to Leverage Proteomics Data for Stratified Medicine"
Stratified medicine, in which information from a patient’s genome is used to help direct their treatment, has revolutionised some areas of clinical practice. However, most diseases do not involve changes in an individual’s genome, so the power of insights derived from genotyping and whole genome sequencing is limited.
Interrogation of a patient’s proteome (the collection of 20,000+ unique proteins that can be extracted from human tissue and body fluids) promises much more fine-grained insight into the state of a patient, because proteins are the key functional molecules of biology. The abundance, location, physical structure and activity of each protein can vary substantially during disease progression, providing a wealth of information for assessing a patient’s state and forming a treatment plan. But how to reliably turn proteomic data into clinical insights remains an open question.
This project will investigate the extent to which artificial intelligence (AI) can be used to leverage proteomics data for stratified medicine. This will be achieved by applying cutting edge data science and AI techniques with the aim of producing reliable and explainable readouts of disease state from clinical proteomics data, overcoming inherent challenges in these data such as heterogeneity, missingness and lack of statistical power.
"What drew me to the HDiP programme was the application of statistical methods to important problems in healthcare coupled with the human centred approach to data."
Supervisors - Prof Isabelle Mareschal, Prof Richard Hooper
"Establishing Links between Eye Gaze and Self-Referential Bias with General Wellbeing and Psychiatric
The early detection of mental health disorders is vital to prevent severe cases and hospitalisations. Therefore, identifying risk factors is important. Abnormal eye gaze (a measure of what one is paying attention to) is linked to traits like social anxiety and depression. However, research tends to focus on one specific disorder. There is a need to examine the relationship between eye-tracking measures and general psychopathology. This will help accelerate research that can identify predictors of psychopathology.
We will collect participants’ eye-gaze while viewing different types of visual stimuli and measure their self-referential bias (SRB) using an gaze perception task. This is the phenomenon where a person thinks others are looking at them or talking about them, which is linked to various conditions (e.g. psychosis).
The first question is whether visual eye-tracking data (and SRB) can predict mental wellbeing and mental health symptomology. Secondly, we will develop a visual training programme designed to reduce problematic SRB and conduct a small study to see whether it shows promise for future research. We want eye-tracking data to be used in psychopathology risk detection and create training programme designed to reduce SRB which can be tested in a future clinical trial.
"The personal development opportunities, network reach, and the level of support and funding that the Wellcome Trust PhD programme offers are one of the best I have seen. In research, collaboration is essential for creativity and success. Being able to conduct mental health research that will have a real impact on health practice is something I want to achieve, and this is something this programme can help support!"
Supervisors - Prof Borislava Mihaylova, Dr Florian Tomini, Dr Anna Di Simoni
"Developing a health economic framework for assessing asthma management strategies in UK primary care"
Asthma is a chronic inflammatory disease of the lungs with high morbidity that represents a major use of healthcare resources. This burden is unevenly distributed across the asthma population, with individuals suffering from more severe forms of the disease having higher morbidity and mortality risks and lower quality of life, accounting for a greater share of resources and costs. There is an urgent need to reduce this burden by appreciating the complexity of asthma and exploring ways to improve its management in view of individual demographic, lifestyle and clinical characteristics and asthma severity.
We aim to develop an evaluative framework for adult asthma that is able to track the long-term health outcomes, quality of life, and healthcare usage and costs of individuals with asthma according to their disease severity status and sociodemographic characteristics, thereby taking into account the diversity of the asthma population and the fact that a ‘one-size-fits-all’ approach to managing or treating asthma is ineffective. The asthma population will be derived from the OPCRD database, which contains comprehensive clinical data on over 18% of the UK population. The model should help to inform key healthcare decisions regarding the efficient management of asthma with focus on UK primary care.
"I was thrilled to join the Health Data in Practice PhD programme in 2020. I’m excited to undertake the MRes year and following PhD in the interdisciplinary and incredibly supportive environment that QMUL offers."
Supervisors - Dr Sarah Finer, Prof David van Heel, Dr Nina Fudge, Dr Magda Osman
"Applying a life course perspective to genetic risk perception for type 2 diabetes in British Bangladeshis
and British Pakistanis"
Research on the communication of genetic risk information for common conditions like type 2 diabetes (T2D) has largely focused on disease identification, prevention and management in older, White European populations. Much remains unknown about the use of genetic risk prediction tools in diverse ethnic and/or age groups—however, emerging work in South Asian populations suggest that such tools may have added benefits for the identification of younger people at risk of developing T2D. We are interested in applying a life course perspective to this research area to examine potential age differences in perceptions and beliefs surrounding T2D—including whether young people perceive unique benefits from acting on their genetic risk status. We will first synthesise currently available evidence on genetic risk communication and illness perceptions. Then, we will provide genetic risk information about T2D to a community-based cohort of British Bangladeshis and British Pakistanis in the UK—integrating the findings from our synthesis—to investigate any age differences in how people evaluate information and formulate ideas about their risk; and compare different formats of risk communication. This research will inform efforts addressing the potential of genetic risk communication for a population disproportionately affected by T2D—yet underrepresented in genetics research.
“Multi-disciplinarity research has always been the focus of my passion and commitment, and I feel very strongly that this aligns well with the framework of the programme. It places prominence in the different methodological issues and challenges surrounding the interdisciplinary nature of health data, and draws from the experience of a very attractive supervisory pool coming from various fields.”
Supervisors - Prof Mike Barnes, Dr Myles Lewis
"Understanding the aetiology and stratification of Immune Mediated Inflammatory Diseases using AI"
Our goal is to use Artificial Intelligence (AI) to better understand the biology of immune-mediated inflammatory diseases (IMIDs) such as rheumatoid arthritis, psoriasis, lupus, and Sjögren’s syndrome. Despite being common conditions, the mechanisms which explain how IMIDs work are poorly understood. We consider there must be key cellular mechanisms underlying IMIDs and that studying the differences between them will provide clues as to their origin and development. This project will focus on studying interferon signalling, a central mechanism for control of inflammation, known to influence IMID development and drug response. Using genomic data from a range of IMIDs, we will build networks explaining the relation between the different components of interferon signalling. We will also integrate patient clinical information, genomic and imaging data to find sub-groups of patients whose disease and response to treatment progresses in a similar manner. Finally, we will use this research to identify pharmaceutical drugs, already on the market and in use for different conditions, which might be effective in the treatment of IMIDs because their mechanism of action indicates they would affect important areas of IMID biology. This could also lead to identifying areas of interest for future IMID drug development research.
"I was immediately drawn to the Wellcome Trust Health data in practice PhD programme because it emphasises the importance of developing a Human-centered data science, understanding how and why data is generated and how it is used by practitioners. I believe these points are key to developing meaningful models and insights, which add value both for the patient and the medical community, and that they are the cornerstone of future advances in AI and Machine Learning applied to health data."
Supervisors - Dr Clare Relton, Dr Anna Di Simoni, Prof Hilary Pinnock
"Exploring the use of routine health data in improving the efficiency of randomised implementation
Trials that assess methods to help implement health interventions of proven effectiveness are known as implementation trials. However, randomised trials tend to be expensive and take many years to conduct. The use of patient health data collected from routine practice, such as GPs, hospitals, and secondary care services, may make these types of trials more efficient. However, there are many challenges such as gaining access to such data and the data might be inaccurate.
In this PhD I will explore how patient health data are used in implementation trials. I plan to start by reviewing reports of these types of trials and consulting the experts in the field. I will survey and interview those who have conducted or have been involved in such trials (e.g., trialists, IT staff, statisticians, researchers, patient participants etc.) and carry out some in-depth case studies. The involvement of patients and public representatives in all stages of the study will be vital. The key outcome will be a set of recommendations on the use of routine health data in studies implementing health interventions into practice. I hope the outputs of this PhD will increase the speed of adopting health interventions of proven effectiveness into real-world.