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

Published online by Cambridge University Press:  28 July 2011

David Dupplaw ,
Madalina Croitoru ,
Srinandan Dasmahapatra ,
Alex Gibb ,
Horacio González-Vélez ,
Miguel Lurgi ,
Bo Hu ,
Paul Lewis  and
Andrew Peet
David Dupplaw*
Affiliation:
Intelligence, Agents, Multimedia Group, School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK; e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Madalina Croitoru*
Affiliation:
Intelligence, Agents, Multimedia Group, School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK; e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Srinandan Dasmahapatra*
Affiliation:
Intelligence, Agents, Multimedia Group, School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK; e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Alex Gibb*
Affiliation:
University of Birmingham, Birmingham B15 2TT, UK; e-mail: [email protected]
Horacio González-Vélez*
Affiliation:
School of Computing and IDEAS Research Institute, Robert Gordon University, St Andrew Street, Aberdeen AB25 1HG, UK; e-mail: [email protected]
Miguel Lurgi*
Affiliation:
MicroArt, SL. Parc Científic de Barcelona C/Baldiri Reixac, 4-6 – 08028 Barcelona, Spain; e-mail: [email protected]
Bo Hu*
Affiliation:
Intelligence, Agents, Multimedia Group, School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK; e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Paul Lewis*
Affiliation:
Intelligence, Agents, Multimedia Group, School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK; e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Andrew Peet*
Affiliation:
University of Birmingham and Birmingham Children's Hospital, Birmingham B4 6NH, UK; e-mail: [email protected]
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Abstract

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.

Type
Articles
Information
The Knowledge Engineering Review , Volume 26 , Special Issue 3: Computational Intelligence for Neuro-Oncological Diagnosis , 28 July 2011 , pp. 247 - 260
Copyright
Copyright © Cambridge University Press 2011

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