Cohort discovery and activity mining for policy impact prediction

面向政策影响预测的队列发现和活动挖掘。该项目旨在通过发现固有的患者队列并评估政策对这些队列的影响，开发一个智能系统框架来预测政策对澳大利亚患者的影响。所提出的方法为构建智能自动化的政策评估工具奠定了理论基础。预期结果是数据驱动的患者群体发现，这可以更准确地确定最有可能从特定政策中受益的患者群体；以及一个预测政策选择效力的模型，它可以通过实现更明智、更有效的政策决策来增加国家卫生系统的可持续性。

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.