A knowledge-rich distributed decision support framework: a case study for brain tumour diagnosis

被引:2
|
作者
Dupplaw, David [1 ]
Croitoru, Madalina [1 ]
Dasmahapatra, Srinandan [1 ]
Gibb, Alex [2 ]
Gonzalez-Velez, Horacio [3 ,4 ]
Lurgi, Miguel [5 ]
Hu, Bo [1 ]
Lewis, Paul [1 ]
Peet, Andrew [6 ,7 ]
机构
[1] Univ Southampton, Multimedia Grp, Sch Elect & Comp Sci, Southampton SO17 1BJ, Hants, England
[2] Univ Birmingham, Birmingham B15 2TT, W Midlands, England
[3] Robert Gordon Univ, Sch Comp, Aberdeen AB25 1HG, Scotland
[4] Robert Gordon Univ, IDEAS Res Inst, Aberdeen AB25 1HG, Scotland
[5] MicroArt SL, Barcelona 08028, Spain
[6] Univ Birmingham, Birmingham B4 6NH, W Midlands, England
[7] Birmingham Childrens Hosp, Birmingham B4 6NH, W Midlands, England
来源
KNOWLEDGE ENGINEERING REVIEW | 2011年 / 26卷 / 03期
关键词
SPECTRA; AGENTS;
D O I
10.1017/S0269888911000105
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The HealthAgents project aims to provide a decision support system for brain tumour diagnosis using a collaborative network of distributed agents. The goal is that through the aggregation of the small data sets available at individual hospitals, much better decision support classifiers can be created and made available to the hospitals taking part. In this paper, we describe the technicalities of the HealthAgents framework, in particular how the interoperability of the various agents is managed using semantic web technologies. On the broad scale the architecture is based around distributed data-mart agents that provide ontological access to hospitals' underlying data that has been anonymized and processed from proprietary formats into a canonical format. Classifier producers have agents that gather the global data from participating hospitals such that classifiers can be created and deployed as agents. The design on a microscale has each agent built upon a generic-layered framework that provides the common agent program code, allowing rapid development of agents for the system. We believe that our framework provides a well-engineered, agent-based approach to data sharing in a medical context. It can provide a better basis on which to investigate the effectiveness of new classification techniques for brain tumour diagnosis.
引用
收藏
页码:247 / 260
页数:14
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