Available
Project number:
2025_A01
Start date:
October 2025
Project themes:
Main supervisor:
Senior Clinical Lecturer, Consultant Neurophysiologist
Co-supervisor:
Professor Mark Richardson
Improving clinical phenotyping in epilepsy using machine learning with free text clinical records and multimodal clinical data
Epilepsy is one of the most common neurological diseases worldwide. Current digitized clinical records contain significant proportions of free-text, requiring slow poring through records to glean a summary of the patient’s journey. This also renders it inaccessible for research and entails labour-intensive manual extraction of information. Faster and better methods to extract these valuable data from clinical notes could lead to better phenotyping, population segmentation, prognostication and management. This project will focus on developing a machine learning approach to extract information from clinical notes and narrative investigational reports, and to integrate the derived insights with multimodal clinical data for downstream applications. In so doing, we will improve our understanding of the spectrum of epilepsy and improve the granularity of our phenotyping, the better to guide prognostication and management of this complex disease. We aim to leverage real-world multimodal health records data across two large unique healthcare systems in the United Kingdom and Singapore to develop and validate the machine learning approaches and models to ensure generalizability across different healthcare systems and populations. We will thus improve ascertainment of seizure control and clinical phenotyping to identify the different patients across the spectrum of epilepsy and build foundations for integration of narrative clinical investigation reports and multimodal data into digital solutions for clinical platforms.
We are now accepting applications for 1 October 2025
How to apply - A*STAR projects
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.
Key dates - A*STAR projects
- Applications submitted by the closing date of Thursday 30 January 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 interview(s). Interviews are projected to take place in March 2025.
- Project selection will be through a panel interview chaired by CDT Directors at EPSRC DRIVE-Health and relevant supervisors from A*STAR Institute followed by informal discussion with prospective supervisors.
- Successful candidates are required to accept their conditional places by 14 April 2025.
- 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 drive-health-cdt@kcl.ac.uk.
