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  Repurposing and enriching cardiovascular risk prediction model to identify people at risk of cancer – UCL (part of Health Data Research UK’s Big Data for Complex Disease Driver Programme)

   Big Data for Complex Diseases (BDCD)

  Dr Floriaan Schmidt  Applications accepted all year round  Funded PhD Project (UK Students Only)

About the Project

Risk-stratified management of cardiovascular disease (CVD), where people without established disease receive preventative interventions and monitoring based on their 10-years predicted risk, has been highly successful to ensure healthcare resources are allocated to those most likely to benefit.

Through the development of tumour-specific medicines, cancer has been on the forefront of personalised medicine. Preventative strategies for cancer have, however, focussed on costly on-size-fits­-all screening programmes, irrespective of the individual cancer risk. While there have been attempts to prioritise screening and prevention strategies to high risk individuals, such as using prostate-specific antigen, there is a general sparsity of risk-based approached for early detection and prevention of cancers in clinical practice.

Despite distinct pathways of disease development, and depending on the type of cancer (e.g. lung cancer, colorectal cancer), CVD risk factors also contributed to the development of cancer. For example, smoking and alcohol use are important risk factors for both diseases, as well as sedentary lifestyle, poor diet and environmental factors such as air pollution. Given the interrelation between risk factors for CVD and risk factors for cancer, as well as the high clinical uptake of risk prediction tools for CVD, we wish to explore to what extent established models for CVD prediction can be repurposed and enriched to consider the onset of cancer.

Eligibility and suitability

Applicants will need:

  • A background in statistics/epidemiology/computer science or related disciplines.
  • An interest in developing their knowledge of statistics and/or machine learning.
  • The ability to work collaboratively with a team of clinical and statistical experts.

Experience would also be valuable in:

  • working computationally with tabular data, and healthcare data in particular, is appreciated.
  • programming languages such as Python or R
  • working genomic, metabolomics, or proteomics.

Click here to apply.

For further information contact  or visit the Health Data Research UK’s website.

Biological Sciences (4) Computer Science (8) Mathematics (25) Medicine (26)