Predicting Treatment Outcomes in Neurological Conditions: A Health Data Approach to Personalised Epilepsy Care

Background:

Epilepsy treatment outcomes can differ significantly among individuals due to various factors, including the type of epilepsy, specific treatments used, and overall health status. While anti-seizure medications are the primary treatment option for many, approximately one-third of people with epilepsy continue to experience uncontrolled seizures despite medication. Understanding the factors that influence how individuals respond to epilepsy treatments is essential for creating more personalised and effective care strategies. This PhD project aims to bridge this gap by employing cutting-edge digital health techniques to analyse treatment responsiveness in epilepsy and related neurological conditions, leveraging extensive patient data from both primary and secondary healthcare sources.

Project Objectives:

The project’s main goals are to harness electronic health records, employ machine learning (ML) and deep learning methods, and create predictive models to better understand and predict treatment outcomes in epilepsy. The key objectives include:

• Utilising National Health Records: Leverage epilepsy-specific Electronic Patient Record (EPR) data to analyse patient responsiveness to anti-seizure medications, using factors such as seizure frequency, patient history, and comorbid conditions to identify patterns in treatment success.
• Analysing Community-Based Healthcare Data: Explore the Centric Health dataset, which offers comprehensive longitudinal data on neurological patients in community care, including medication details, lab results, and consultation records. This will involve advanced natural language processing (NLP) techniques to extract information from unstructured clinical notes, further enhancing the dataset’s utility.
• Digital Twin Development: Develop digital twin models for specific patients with treatment-resistant epilepsy, creating virtual representations that can simulate and predict individual treatment responses, enabling a tailored approach to managing their condition.
• Predictive Modelling with ML: Other approaches may include the application of robust and ethical ML techniques, such as regression models, decision trees, and neural networks, to develop and validate predictive models of treatment responsiveness. The project will use cross-validation and external validation to ensure robustness and generalisability of these models, making them suitable for clinical use.
• Clinical Utility and Evaluation: Conduct a pilot study to assess the practical value of these models in clinical settings, providing real-time insights to healthcare professionals. This will include qualitative feedback from clinicians to evaluate the models’ effectiveness in supporting decision-making processes in epilepsy care.

Methodology:

The PhD student will engage in a diverse range of methodologies, including quantitative and qualitative research techniques. The student will gain skills in health informatics, data science, and digital twin development, positioning them at the forefront of digital health innovation.

Collaboration and Professional Development:

This project is embedded within the dynamic research environment of the FutureNeuro Research Ireland Centre for Translational Brain Science, offering interdisciplinary collaboration opportunities. The PhD student will work closely with clinicians, gaining insights from the front lines of epilepsy care. Additional benefits include public and patient involvement in research, public outreach and education, annual conference attendance, and extensive professional development opportunities through FutureNeuro’s extensive network.

Mandatory Specifications

• MSc in health informatics, computer science, statistics, data science, or a related discipline
• Experience in database management, particularly with health data or in a clinical setting.
• Analytical and technical skills proficiency in using statistical software (i.e. R, Stata etc.)
• Proven track record of managing data in compliance with GDPR and other data protection standards.

Desirable Specifications

• Experience in working with health research ethics committees and understanding of ethical approval processes.
• Excellent communication skills, capable of effectively interacting with a diverse group of technical and non-technical stakeholders.
• Ability to work collaboratively in a fast-paced, research-oriented environment.
• Project management skills: ability to ensure that project plans are communicated and that all timelines are met.
• Self-starter with the ability to work effectively as part of a team; is cordial, tolerant and willing to help others; is cooperative and patient; shares work and information; establishes rapport, can influence and develop effective networks
• Conscientious: has a pro-active approach to work, anticipating and resolving problems in advance; has keen attention to detail, from anticipating and addressing issues in advance to understanding requests and delivering quality work with minimal errors
• Flexibility: can operate flexibly within a busy environment, can shift focus when required

Project Supervisors:

• Prof Kathleen Bennett, School of Population Health, RCSI University of Medicine and Health Sciences
• Prof. Norman Delanty, FutureNeuro and Beaumont Hospital, Dublin

How to Apply

To apply, please access the following link and complete the application form in full:  

https://forms.office.com/e/6YHmSkW7Eg

• Unfortunately, we are unable to provide individual feedback to applicants.
• Shortlisted candidates will be invited for interview.
• At this stage only successful candidates will be contacted to submit CV, transcripts and other relevant documentation.
• For successful candidates, referees will also be contacted at this stage for a reference.

 Applications must include: 

(i) a completed application form 

(ii) English language requirements – see https://www.rcsi.com/dublin/postgraduate/policies-and-guidelines/english-language-requirements. 

Deadline: All applications must be made online by 19 February 2025

It is the candidate’s responsibility to ensure the application form is completed in full and on time. Late and/or incomplete applications will not normally be assessed.

Further information on the Royal College of Surgeons in Ireland can be found here: https://www.rcsi.com/ 

Expected shortlisting date: 24 February 2025

Expected interview date: 11 March 2025

Projected start date of this position: 01 April 2025