Primary supervisor
Jesmin NaharResearch area
Machine LearningBackground
Endometriosis and Polycystic Ovary Syndrome (PCOS) are complex conditions with diverse clinical presentations and potentially long-term health impacts. Variability in symptoms and disease progression can make early identification and risk assessment challenging. The increasing availability of clinical, demographic, hormonal, metabolic and longitudinal health data provides opportunities for Artificial Intelligence (AI) and Machine Learning (ML) to support earlier and more personalised healthcare.
Research Aim
This PhD research aims to develop advanced AI and Machine Learning methods for predictive modelling, clinical decision support and longitudinal risk forecasting for endometriosis and PCOS.
Research Approach
The research will investigate clinical, demographic, hormonal, metabolic, lifestyle and, where available, longitudinal patient data. Machine-learning and deep-learning methods will be developed and evaluated for early prediction, risk stratification and prognostic modelling of endometriosis and PCOS.
Prediction and Decision Support
The research will develop AI-based models to identify individuals at increased risk and provide interpretable predictions that could support earlier clinical assessment and personalised healthcare decision-making.
Longitudinal Forecasting
Where suitable longitudinal data are available, temporal and predictive modelling approaches will be investigated to forecast disease-related risk trajectories, outcomes and progression over time.
Explainable and Responsible AI
Explainable AI (XAI) will be explored to identify important factors contributing to predictions and improve model transparency. The research will also consider model robustness, fairness, generalisability, uncertainty and responsible use of AI in women's healthcare.
Expected Contribution
The project aims to develop a robust, interpretable and generalisable AI framework integrating prediction, clinical decision support and longitudinal forecasting to support earlier identification and more personalised management of endometriosis and PCOS.
Required knowledge
Required Knowledge
Students should have a strong background in:
- Data Science, Artificial Intelligence and Machine Learning
- Python and/or R programming
- Data preprocessing, feature engineering and statistical analysis
- Supervised and unsupervised machine-learning techniques
- Deep learning and neural networks
- Predictive, risk and prognostic modelling
- Temporal and longitudinal data analysis and forecasting
- Model validation and performance evaluation
- Explainable AI (XAI) and interpretable machine learning
- Healthcare, clinical or biomedical data analysis
- Knowledge of women's health or reproductive health is desirable
- Research methodology, critical thinking and scientific communication
- Responsible, ethical and fair AI in healthcare
