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


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.

Copyright © Cambridge University Press 2011

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Annicchiarico, R., Cortés, U., Urdiales, C. (eds) 2008. Agent technology and e-health. In Whitestein Series in Software Agent Technologies and Autonomic Computing. Birkhäuser Verlag.Google Scholar
Arús, C., Celda, B., Dasmahapatra, S., Dupplaw, D., González-Vélez, H., van Huffel, S., Lewis, P., Lluch i Ariet, M., Mier, M., Peet, A., Robles, M. 2006. On the design of a web-based decision support system for brain tumour diagnosis using distributed agents. In 2006 IEEE/WIC/ACM International Conference on Web Intelligence & Intelligent Agent Technology (WI-IAT 2006 Workshop), IEEE, 208211.Google Scholar
Beckett, D. 2004. Modernising semantic web markup. In XML Europe 2004, Dumbill E. (ed.). IDEA Alliance, Scholar
Beckett, D. 2007. Turtle—Terse RDF triple language. Manual (version 20 November 2007), ILRT University of Bristol, Scholar
Bellifemine, F., Poggi, A., Rimassa, G. 2001. JADE: a FIPA2000 compliant agent development environment. In AGENTS'01: Fifth International Conference on Autonomous Agents. ACM Press, Montreal, Canada, 216217.CrossRefGoogle Scholar
Bizer, C. 2006. D2RQ. Freie Universität Berlin. (accessed 13 February 2007).Google Scholar
Boniface, M., Leonard, T., Surridge, M., Taylor, S., Finlay, L., Mccorry, D. 2005. Accessing patient records within virtual healthcare organisations. In eChallenges 2005: Innovation and the Knowledge Economy: Issues, Applications, Case Studies, Cunningham, P. & Cunningham, M. (eds). ISBN 1-58603-563-0. IOS Press, Ljubljana, Slovenia.Google Scholar
Broekstra, J., Kampman, A., van Harmelen, F. 2002. Sesame: a generic architecture for storing and querying RDF and RDF schema. In First International Semantic Web Conference, Lecture Notes in Computer Science 2342, 5468. Springer Verlag.Google Scholar
Brugali, D., Sycara, K. 2000. Towards agent oriented application frameworks. ACM Computing Survey 32(1), 2127.CrossRefGoogle Scholar
Brugali, D., Menga, G., Aarsten, A. 1997. The framework life span. Communications of the ACM 40(10), 6568.CrossRefGoogle Scholar
DeAngelis, L. M. 2001. Brain tumors. New England Journal of Medicine 344(2), 114123.CrossRefGoogle ScholarPubMed
Georgiadis, P., Cavouras, D., Kalatzis, I., Daskalakis, A., Kagadis, G. C., Sifaki, K., Malamas, M., Nikiforidis, G., Solomou, E. 2008. Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features. Computer Methods and Programs in Biomedicine 89(1), 2432.CrossRefGoogle ScholarPubMed
González-Vélez, H., Mier, M., Julià-Sapé, M., Arvanitis, T. N., García-Gómez, J. M., Robles, M., Lewis, P. H., Dasmahapatra, S., Dupplaw, D., Peet, A., Arús, C., Celda, B., Van Huffel, S., Lluch-Ariet, M. 2009. HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis. Applied Intelligence 30(3), 191202.CrossRefGoogle Scholar
Gruber, T. R. 1993. A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199220.CrossRefGoogle Scholar
Huang, J., Jennings, N., Fox, J. 1995. Agent-based approach to health care management. Applied Artificial Intelligence 9(4), 401420.CrossRefGoogle Scholar
Lanzola, G., Gatti, L., Falasconi, S., Stefanelli, M. 1999. A framework for building cooperative software agents in medical applications. Artificial Intelligence in Medicine 16(3), 223249.CrossRefGoogle ScholarPubMed
Merelli, E., Armano, G., Cannata, N., Corradini, F., d′Inverno, M., Doms, A., Lord, P., Martin, A., Milanesi, L., Möller, S., Schroeder, M., Luck, M. 2007. Agents in bioinformatics, computational and systems biology. Briefings in Bioinformatics 8(1), 4559.CrossRefGoogle ScholarPubMed
Rees, J. 2003. Advances in magnetic resonance imaging of brain tumours. Current Opinion in Neurology 16(6), 643650.CrossRefGoogle ScholarPubMed
Robertson, D. 2004. Multi-agent coordination as distributed logic programming. In International Conference on Logic Programming, Lecture Notes in Computer Science 3132, 416430. Springer.CrossRefGoogle Scholar
Shadbolt, N., Lewis, P., Dasmahapatra, S., Dupplaw, D., Hu, B., Lewis, H. 2004. MIAKT: combining grid and web services for collaborative medical decision making. In UK e-Science All Hands Meeting, Cox S. (ed.). EPSRC, Nottingham, UK, 784791. Scholar
Tate, A. R., Underwood, J., Acosta, D. M., Julià-Sapé, M., Majós, C., Moreno-Torres, A., Howe, F. A., van der Graaf, M., Lefournier, V., Murphy, M. M., Loosemore, A., Ladroue, C., Wesseling, P., Bosson, J. L., Cabañas, M. E., Simonetti, A. W., Gajewicz, W., Calvar, J., Capdevila, A., Wilkins, P. R., Bell, B. A., Rémy, C., Heerschap, A., Watson, D., Griffiths, J. R., Arús, C. 2006. Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. NMR in Biomedicine 19(4), 411434.Google Scholar
Tatter, S. B. 1996. The New WHO Classification of Tumors Affecting the Central Nervous System. PhD thesis, Cornell University Medical College/The Rockefeller University. Scholar
Turck, F. D., Decruyenaere, J., Thysebaert, P., Hoecke, S. V., Volckaert, B., Danneels, C., Colpaert, K., Moor, G. D. 2007. Design of a flexible platform for execution of medical decision support agents in the intensive care unit. Computers in Biology and Medicine 37(1), 97112.CrossRefGoogle ScholarPubMed
Weiss, S. 1994. Histological typing of soft tissue tumours. In World Health Organization International Histological Classification of Tumours, 2nd edn. ISBN 978-3-540-56794-3. Springer.Google Scholar
Wright, A. J., Arús, C., Wijnen, J. P., Moreno-Torres, A., Griffiths, J. R., Celda, B., Howe, F. A. 2008. Automated quality control protocol for MR spectra of brain tumors. Magnetic Resonance in Medical Sciences 59(6), 12741281.CrossRefGoogle ScholarPubMed