I studied medicine at the University of Galway, Ireland, then worked as a doctor for a number of years before completing an MSc in Health Data Analytics and Machine Learning at Imperial College London. My research interests lie in risk prediction for non-communicable diseases, causal machine learning, and multimodal real world data.
Research Driver Programme: Big Data for Complex Diseases
Title: Repurposing And Enriching Cardiovascular Risk Prediction Models To Identify People At Risk Of Cancer and Other Chronic Diseases.
Doctors often use certain tools to figure out if someone is at risk of having a heart attack or stroke. My project explores whether we can use these same tools to predict if a person might develop other heart problems, like atrial fibrillation (a type of irregular heartbeat), heart failure, or long-term illnesses such as cancer, dementia (which can cause memory loss), and COPD (a serious lung disease that makes it hard to breathe).
We already know that conditions like heart attacks, strokes, and cancer share some common risk factors. What I want to find out is whether the methods we use to predict heart-related issues could also tell us who’s at risk of developing these other health problems.
I also plan to see if these prediction tools can be improved by adding new kinds of information, for example, though looking at the amounts of fats or proteins in someone’s blood.
Lastly, I’ll use a relatively new research method, known as target trial emulations, to see if medications that are usually given for heart disease might also influence the risk of getting other long-term illnesses.
What is your motivation for undertaking this project and how will this funding impact your research?
From my experience working as a clinical doctor, I realised the profound impact that early detection and prevention strategies can have on patient outcomes, especially in chronic diseases like cardiovascular disease (CVD) and cancer. By developing improved prediction tools for CVD-related diseases, I hope to contribute to a reduction in the rates of premature mortality and disease-related morbidity through targeted treatment strategies and informed public health policy.
The funding from this program will grant me access to some of the best resources available to conduct this research, including extensive epidemiological datasets with linked, multimodal patient information. Through the BDCD programme, I have the chance to work with experts in the fields of epidemiology, precision medicine, and multimorbidity, providing me with unique opportunities for collaborative learning. Finally, the funding will facilitate dissemination of results at conferences and in scientific journals, which will promote the integration of my findings into clinical practice.
Big Data for Complex Disease
We will harness whole population, national linked health data to improve the prevention, diagnosis and treatment of cardiovascular disease, cancer, and other complex diseases