Clinical Machine Learning
Reproducible workflows for assisted-reproduction data, including outcome definition, feature construction and patient-aware, time-aware validation.
Real-world data in assisted reproduction
Since June 2026, I have worked as a Research Assistant at the University of Surrey with Dr Xilu Wang, developing reproducible machine-learning workflows for large, heterogeneous clinical datasets in assisted reproduction and in vitro fertilisation (IVF).
This ongoing research focuses on making observational data suitable for reliable outcome modelling:
- Outcome definition: define clinically meaningful endpoints while accounting for incomplete records and differences in outcome availability.
- Feature construction: create analysis-ready features that respect the longitudinal structure of treatment cycles.
- Validation: design patient-aware and time-aware evaluation to reduce information leakage and assess model generalisation.
- Reproducibility: maintain auditable workflows with attention to data provenance and the confidential handling of de-identified healthcare data.
The work extends my research in uncertainty-aware modelling and temporal prediction to real-world clinical observations.