This project aims to develop physician decision-support models, focusing on critical clinical decision points for diseases with relatively high rates of diagnostic error. We will apply standard statistical, machine learning and causal inference techniques to sociodemographic data, healthcare records, and familial risk histories to analyse health trajectories and patterns of clinical decisions to develop risk models to support physician decision-making.
