Cohort discovery and activity mining for policy impact prediction
Description: Cohort discovery and activity mining for policy impact prediction. This project aims to develop an intelligent systematic framework to predict policy impacts on Australian patients, by discovering inherent patient cohorts and assessing the impact of the policies on these cohorts. The proposed methods lay the theoretical foundations for building intelligent automated tools for policy assessment. Expected outcomes are data-driven patient group discovery, which could more precisely identify the patient cohorts most likely to benefit from a specific policy; and a model to predict the efficacy of policy options, which could increase the sustainability of the national health system by enabling smarter, more efficient policy decision-making.
Funder: Australian Research Council
Amount: A$542,500
Funding type: research
Funder scheme: Linkage Projects
Start date: 2016-01-01
End date: 2020-07-18
Investigator: Chengqi Zhang
Institution: University of Technology Sydney
Funded outputs count: 14
Funder award ID: LP160100630
OpenAlex ID: G4116957712