Background:
Migraine, other headache disorders and facial pain, are the leading cause of disability amongst neurological conditions. Yet diagnosis, treatment selection, and long-term management remain inconsistent, with frequent misclassification and delayed recognition of chronic transformation. These inefficiencies contribute to poor patient outcomes, escalating mental health comorbidity, and unnecessary use of secondary care services.
The increasing availability of linked multimodal electronic health records (EHR) data across the care spectrum provides a transformative opportunity to apply artificial intelligence (AI) tools to identify hidden patterns in disease trajectories, treatment response, and clinical signatures of poor outcomes. Leveraging these data through explainable AI could enable earlier diagnosis, personalised prescribing, and data-informed triage pathways between primary and secondary
Novelty and Importance:
The Guy’s and St Thomas’ headache services and the National Audit of Headache Disorders (NAHD) at KCL contain rich EHRs and offer a unique opportunity to study headache outcomes by integrating disease characteristics data with treatment outcomes and with structural brain imaging. This multimodal approach provides a deeper understanding of the biological mechanisms underlying headache and facial pain conditions. This research will also contribute to the development of personalized prescribing, ultimately improving patient outcomes by uncovering patterns in treatment effectiveness.
Aims and Objectives:
Aim: To apply multimodal AI models to linked national audit and EHR data to predict treatment outcomes and chronic transformation in headache and facial pain disorders, integrating clinical, demographic, and neuroimaging data to inform personalised management strategies and NHS decision-support systems.
Objectives:
1. Apply NLP to extract phenotypes and symptom trajectories from free-text data; develop multimodal embeddings combining clinical, medication, and imaging features.
2. Build and validate deep learning models (e.g. transformer architectures, graph neural networks) to predict treatment response, relapse, and chronic headache patterning.
3. Use causal inference (e.g. targeted maximum likelihood estimation, causal discovery) and explainability frameworks (e.g. SHAP) to identify modifiable risk factors and interpretable drivers of poor outcomes.
4. Co-design digital prototypes for NHS headache services that translate predictive insights into real-time, clinician-facing decision-support tools.