Available
Project number:
2025_101
Start date:
October 2025
Project themes:
Main supervisor:
Professor of Medical Bioinformatics
Co-supervisor:
Additional Information:
Machine learning in electronic health records for prognosis and diagnosis of rare neurological disorders
Background: Electronic Health Records (EHRs) hold detailed longitudinal information about each patient's health status and disease progression, the majority of which (~80%) are stored within unstructured text. This data provides the opportunity to transform care through learning from the data available on other patients. Rare neurological conditions are challenging in that they often have diverse clinical presentations, with a relatively small and variable number of patients in different healthcare systems. Technology Overview: This project will develop Machine Learning (ML) workflows for automated diagnosis, prognosis and modeling of trajectories associated with rare neurological disorders, leveraging EHRs and natural language processing, and accounting for limited and imbalanced data environments. The work will also leverage the emerging field of Digital Twins, the idea that we can use AI and large amounts of digital data within EHRs to accurately mimic real-world patients allowing users to model possible scenarios and outcomes on the twin’s real-world counterpart. As the patient data landscape is increasingly multimodal, models that can aggregate multiple data streams in EHR data are desirable. Characterisation of the rare neurological conditions will leverage existing tools already developed and deployed within partner Trusts such as CogStack, a platform which has a near real-time feed from the EHR system within KCH (>1m patients), SLaM and GSTT, MedCAT for Named Entity Recognition and Linking of the text, and Foresight, a novel transformer-based pipeline that uses a GPTv2/3 language modelling approach to structure and organize EHRs.
We are now accepting applications for 1 October 2025
How to apply
Candidates should possess or be expected to achieve a 1st or upper 2nd class degree in a relevant subject including the biosciences, computer science, mathematics, statistics, data science, chemistry, physics, and be enthusiastic about combining their expertise with other disciplines in the field of healthcare.
Important information for International Students:
It is the responsibility of the student to apply for their Student Visa. Please note that the EPSRC DRIVE-Health studentship does not cover the visa application fees or the Immigration Health Surcharge (IHS) required for access to the National Health Service. The IHS is mandatory for anyone entering the UK on a Student Visa and is currently £776 per year for each year of study. Further detail can be found under the International Students tab below.
Next Steps
- Applications submitted by the closing date of Thursday 6 February 2025 will be considered by the CDT. We will contact shortlisted applicants with information about this part of the recruitment process.
- Candidates will be invited to attend an interview. Interviews are projected to take place in April 2025.
- Project selection will be through a panel interview chaired by either Professor Richard Dobson and Professor Vasa Curcin (CDT Directors) followed by informal discussion with prospective supervisors.
- If you have any questions related to the specific project you are applying for, please contact the main supervisor of the project directly.
For any other questions about the recruitment process, please email us at
