Efficient data mining methods for evidence-based decision making

用于循证决策的高效数据挖掘方法

• 批准号: DP170101306
• 负责人: Prof Jiuyong Li
• 金额: $ 26.66万
• 依托单位: University of South Australia
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2017
• 资助国家: 澳大利亚
• 起止时间: 2017-06-29 至 2021-06-28
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP170101306
• 关键词: Efficient; data; mining; methods; evidence

This project aims to develop efficient data mining methods for causal predictions. Evidence-based decision making (EBD), such as evidence-based medicine and policy, is always preferable. To support EBD, causal predictions forecast how outcomes change when conditions are manipulated. Progress has been made in theoretical research on causal inference based on observational data, but few methods can automatically mine causal signals from the data and methods for efficient causal predictions based on data are even fewer. This project will apply its methods to biomedical problems. The outcomes could support smart and data-driven evidence based decision making in many areas, such as therapeutics and government policy making.

该项目旨在开发用于因果预测的有效数据挖掘方法。循证决策（EBD），如循证医学和政策，总是更可取的。为了支持EBD，因果预测预测了当条件被操纵时结果如何变化。基于观测数据的因果推理理论研究取得了一定的进展，但很少有方法能够从数据中自动挖掘出因果信号，基于数据进行有效因果预测的方法更是少之又少。这个项目将把它的方法应用于生物医学问题。这些结果可以在许多领域支持智能和数据驱动的基于证据的决策，例如治疗和政府政策制定。