Preventing Multiple Sclerosis: AI-Driven Identification of Modifiable Exposures - A Population-Scale Analysis Using OpenSAFELY and Target-Trial Emulation
Not stated
- Funding
- Competition Funded PhD Project (Students Worldwide)
- Application deadline
- 8 December 2026
About the project
About the Project Summary Multiple sclerosis (MS) is a major cause of neurological disability in young adults, and emerging evidence suggests that intervening before clinical onset may prevent or delay disease. This project will use OpenSAFELY, containing longitudinal primary care data from approximately 58 million people in England, to identify modifiable environmental, lifestyle and therapeutic factors associated with MS risk. It will combine machine learning to discover candidate exposures with causal inference and target-trial emulation to determine which are most likely to influence MS onset. The goal is to identify actionable prevention strategies for future clinical trials and NHS prevention programmes. Work will span 3 leading centres: the Cambridge Clinical MS Research Group (Will Brown), the Cambridge Centre for AI in Medicine (Mihaela van Der Schaar) and The University of Bristol (Jonathan Sterne). Project Aims 1. Identify candidate modifiable factors associated with MS risk. Use population-scale OpenSAFELY data, existing evidence and machine-learning approaches to investigate environmental, lifestyle and therapeutic factors associated with incident MS. 2. Estimate the causal effects of promising exposures on MS onset. Apply contemporary causal-inference methods and target-trial emulation to determine whether selected exposures are likely to causally influence the subsequent development of MS. 3. Prioritise interventions for future MS prevention trials. Identify modifiable exposures with the strongest evidence for a preventive effect and prioritise candidate interventions for testing in prospective prevention trials. Research Themes ; Multiple sclerosis prevention and epidemiology Artificial intelligence and machine learning Causal inference and target-trial emulation Population-scale electronic health records and real-world data Modifiable environmental and lifestyle risk factors Precision and preventive medicine. How to Apply; If you are interested in this project, please go to the University pages and apply via the online portal; PhD https://www.postgraduate.study.cam.ac.uk/courses/directory/cvcnpdpcn/apply Research MPhil https://www.postgraduate.study.cam.ac.uk/courses/directory/cvcnmpmds/apply