title: PHARM - Association Rule Mining for Predictive Health.
abstract: Predictive health is a new and innovative healthcare model that focuses on maintaining health rather than treating diseases. Such a model may benefit from computer-based decision support systems, which provide more quantitative health assessment, enabling more objective advice and action plans from predictive health providers. However, data mining for predictive health is more challenging compared to that for diseases. This is a reason why there are relatively fewer predictive health decision support systems embedded with data mining. The purpose of this study is to research and develop an interactive decision support system, called PHARM, in conjunction with Emory Center for Health Discovery and Well Being (CHDWB®). PHARM adopts association rule mining to generate quantitative and objective rules for health assessment and prediction. A case study results in 12 rules that predict mental illness based on five psychological factors. This study shows the value and usability of the decision support system to prevent the development of potential illness and to prioritize advice and action plans for reducing disease risks.
authors: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors: Chih-Wen
authors: CW
authors: Cheng
authors: Center for Health Discovery and Well Being, Emory-Georgia Tech Predictive Health Institute, Atlanta, GA, USA.
authors: Greg S
authors: GS
authors: Martin
authors: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors: Po-Yen
authors: PY
authors: Wu
authors: Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
authors: May D
authors: MD
authors: Wang
keywords: Decision support system; association rule mining; health modeling and prediction; predictive health