This PhD research applies machine learning to electronic health records (EHRs) to predict and manage gestational diabetes mellitus (GDM). The PhD is divided into four projects:

1. Early Prediction: Developing models to predict GDM during the first trimester.
2. Historical Analysis: Using previous pregnancy data to improve GDM prediction.
3. Clinical Deployment: Implementing these models in a national maternity hospital for real-time GDM risk assessment and clinical evaluation.
4. Treatment Optimisation: Predicting the GDM treatment paths based on EHR data.

The research aims to enhance early intervention, risk management, and treatment in maternal healthcare through machine learning modelling.