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

A knowledge-rich distributed decision support framework: a case study for brain tumour diagnosis
A knowledge-rich distributed decision support framework: a case study for brain tumour diagnosis
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 datasets 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 inter-operability 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 anonymised 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 micro-scale 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 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.
247-260
Dupplaw, David
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Croitoru, Madalina
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Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698
Gibb, Alex
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Gonzalez-Velez, Horacio
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Lurgi, Miguel
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Hu, Bo
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Lewis, Paul
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Peet, Andrew
0e112646-47cf-46bb-aec9-c5a04e9a27ab
Dupplaw, David
c563ca2b-756a-4d3f-bf99-4f60bb2be1ce
Croitoru, Madalina
4a2ca559-8d8f-4b3d-8622-c2f83796dadc
Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698
Gibb, Alex
98688a75-a6d1-4ba3-9c6e-927434d47117
Gonzalez-Velez, Horacio
26055533-6f8e-4d8f-bffb-42fd7911c0f4
Lurgi, Miguel
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Hu, Bo
927680e6-b2b4-4b88-9a6e-20a86bb9d2d2
Lewis, Paul
7aa6c6d9-bc69-4e19-b2ac-a6e20558c020
Peet, Andrew
0e112646-47cf-46bb-aec9-c5a04e9a27ab

Dupplaw, David, Croitoru, Madalina, Dasmahapatra, Srinandan, Gibb, Alex, Gonzalez-Velez, Horacio, Lurgi, Miguel, Hu, Bo, Lewis, Paul and Peet, Andrew (2011) A knowledge-rich distributed decision support framework: a case study for brain tumour diagnosis. [in special issue: Computational Intelligence for Neuro-Oncological Diagnosis] The Knowledge Engineering Review, 26 (3), 247-260. (doi:10.1017/S0269888911000105).

Record type: Article

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 datasets 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 inter-operability 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 anonymised 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 micro-scale 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 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.

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e-pub ahead of print date: 28 July 2011
Published date: July 2011
Organisations: Web & Internet Science, Southampton Wireless Group

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Local EPrints ID: 271046
URI: https://eprints.soton.ac.uk/id/eprint/271046
PURE UUID: 4591e33e-7e90-4466-a25e-415c03e732b6

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Date deposited: 10 May 2010 13:04
Last modified: 19 Jul 2019 22:14

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Contributors

Author: David Dupplaw
Author: Madalina Croitoru
Author: Srinandan Dasmahapatra
Author: Alex Gibb
Author: Horacio Gonzalez-Velez
Author: Miguel Lurgi
Author: Bo Hu
Author: Paul Lewis
Author: Andrew Peet

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