Distributed data mining in a ubiquitous healthcare framework

被引:0
作者
Viswanathan, M. [1 ]
机构
[1] Carnegie Mellon Univ, H John Heinz III Sch Publ Policy & Mgt, Adelaide, SA, Australia
来源
ADVANCES IN ARTIFICIAL INTELLIGENCE | 2007年 / 4509卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Ubiquitous Healthcare (u-healthcare) which focuses on automated applications that can provide healthcare to citizens anywhere/anytime using wired and wireless mobile technologies is becoming increasingly important. Ubiquitous healthcare data provides a mine of hidden knowledge which can be exploited in preventive care and "wellness" recommendations. Data mining is therefore a significant aspect of such systems. Distributed Data mining (DDM) techniques for knowledge discovery from databases help in the thorough analysis of data collected from healthcare facilities enabling efficient decision-making and strategic planning. This paper presents and discusses the development of a prototype ubiquitous healthcare system. The prospects for integrating data mining into this framework are studied using a distributed data mining system. The DDM system employs a mixture modelling mechanism for data partitioning. Initial results with some standard medical databases offer a plausible outlook for future integration.
引用
收藏
页码:261 / 271
页数:11
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