Cohort discovery and activity mining for policy impact prediction

用于政策影响预测的群组发现和活动挖掘

• 批准号: LP160100630
• 负责人: Prof Chengqi Zhang
• 金额: $ 36.17万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-07-18 至 2020-07-18
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP160100630
• 关键词: Cohort; discovery; activity; mining; policy

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.

用于政策影响预测的队列发现和活动挖掘。该项目旨在开发一个智能系统框架，通过发现固有的患者队列并评估政策对这些队列的影响，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化的政策评估自动化工具奠定了理论基础。预期成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;以及预测政策选项有效性的模型，这可以通过实现更智能，更有效的政策决策来提高国家卫生系统的可持续性。