Project Overview
The project will look at improving the accuracy and generalisability of risk prediction models by accounting for heterogeneity from various sources (such as systematic differences in important risk factors not included in the models). The project will also look at how to exploit the way in which patients interact with health services to improve the predictive performance, by drawing information from the frequency and timing of the data collected within the electronic health records (EHR’s). A key limitation of current risk prediction models is that they do not allow consideration of ‘what-if’ scenarios. In this project, to enable ‘what-if’ queries, a potential outcomes (causal) framework will be used to incorporate counterfactuals (such as medication use, surgeries, and lifestyle changes) into a clinical prediction model.