Research Grant
[Cite as https://purl.org/au-research/grants/arc/LP160100630]
Researchers:
Chengqi Zhang
(Chief Investigator)
,
GUODONG LONG
(Chief Investigator)
,
Yi Yang
(Chief Investigator)
,
Dr Allison Clarke
(Partner Investigator)
,
Dr Nicky Antonius
(Partner Investigator)
View all 7 related researchers
Brief description Cohort discovery and activity mining for policy impact prediction. 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.
Funding Amount $520,000
Funding Scheme Linkage Projects
- ARC : LP160100630
- PURL : https://purl.org/au-research/grants/arc/LP160100630
