Zoe Hancox
- Position: Postdoctoral Research Fellow
- Areas of expertise: graph neural networks; health data science; machine learning; electronic health records
- Email: Z.L.Hancox@leeds.ac.uk
- Location: 10.31 Worsley
- Website: LinkedIn | Googlescholar | Researchgate | ORCID
Profile
Zoe studied Medical Engineering at the University of Bradford and at the University of Leeds. Zoe completed her MSc/PhD in AI for Medical Diagnosis and Care in 2025, her thesis title was “Temporal Graph-based Convolutional Neural Neworks for Electronic Health Records” where she developed models to predict hip and knee replacement risk up to 5 years in advance using explainable graph-based methods.
Research interests
Zoe has experience working with electronic health record data and machine learning/ artificial intelligence models to predict individual patient risks and outcomes. Zoe is currently involved in a project (PREDICT) looking at detecting and repairing models in healthcare which have sufferered from performance deterioration due to temporal drift.
Qualifications
- MSc and PhD in Artificial Intelligence for Medical Diagnosis and Care, University of Leeds
- MSc in Medical Engineering, University of Leeds
- BEng in Biomedical Engineering, University of Leeds
Student education
Zoe is a co-leader on a health-centred machine learning module within Leeds Institute for Health Sciences and is an organiser of the LIDA Health Early Career Researcher network.