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A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents

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A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents

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dc.contributor.author Sáez Silvestre, Carlos es_ES
dc.contributor.author García Gómez, Juan Miguel es_ES
dc.contributor.author Vicente Robledo, Javier es_ES
dc.contributor.author Tortajada Velert, Salvador es_ES
dc.contributor.author Luts, Jan es_ES
dc.contributor.author Dupplaw, David es_ES
dc.contributor.author Van Huffel, Sabine es_ES
dc.contributor.author Robles Viejo, Montserrat
dc.date.accessioned 2014-12-22T10:07:09Z
dc.date.available 2014-12-22T10:07:09Z
dc.date.issued 2011-09
dc.identifier.issn 0269-8889
dc.identifier.uri http://hdl.handle.net/10251/45653
dc.description.abstract [EN] New biomedical technologies enable the diagnosis of brain tumours by using non-invasive methods. HealthAgents is a European Union-funded research project that aims to build an agent-based distributed decision support system (dDSS) for the diagnosis of brain tumours. This is achieved using the latest biomedical knowledge, information and communication technologies and pattern recognition (PR) techniques. As part of the PR development of HealthAgents, an independent and automatic classification framework (CF) has been developed. This framework has been integrated with the HealthAgents dDSS using the HealthAgents agent platform. The system offers (1) the functionality to search for distributed classifiers to solve specific questions; (2) automatic classification of new cases; (3) instant deployment of new validated classifiers; and (4) the ability to rank a set of classifiers according to their performance and suitability for the case in hand. The CF enables both the deployment of new classifiers using the provided Extensible Markup Language(1) classifier specification, and the inclusion of new PR techniques that make the system extensible. These features may enable the rapid integration of PR laboratory results into industrial or research applications, such as the HealthAgents dDSS. Two classification nodes have been deployed and they currently offer classification services by means of dedicated servers connected to the HealthAgents agent platform: one node being located at the Katholieke Universiteit Leuven, Belgium and the other at the Universidad Politecnica de Valencia, Spain. These classification nodes share the current set of brain tumour classifiers that have been trained from in vivo magnetic resonance spectroscopy data. The combination of the CF with a distributed agent system constitutes the basis of the brain tumour dDSS developed in HealthAgents. es_ES
dc.description.sponsorship This work was partially funded by the European Commission: HealthAgents (contract no. FP6-2005-IST 027214). Jan Luts is a PhD student supported by an IWT grant. Carlos Saez, Salvador Tortajada and Javier Vicente are PhD students partially supported by the Programa Torres Quevedo from the Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ-08-01-06817, PTQ-08-01-06802, PTQ05-02-03386). We thank INTERPRET partners for their support and for providing the data used for training some of the classifiers included in the HealthAgents network; in particular we thank C. Majos (IDI-Bellvitge), John Griffiths (SGUL), Arend Heerschap (RU), Witold Gajewicz (MUL), Jorge Calvar (FLENI), Margarida Julia-Sape (UAB) and Carles Arus (UAB). The language revision of this document was funded by the Universidad Politecnica de Valencia. This work has been partially supported by the Health Institute Carlos III through the RETICS Combiomed, RD07/0067/2001.
dc.language Inglés es_ES
dc.publisher Cambridge University Press (CUP) es_ES
dc.relation MEC/PTQ-08-01-06817 es_ES
dc.relation.ispartof Knowledge Engineering Review es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Decision-support-system es_ES
dc.subject Component analysis es_ES
dc.subject Spectra es_ES
dc.subject Cancer es_ES
dc.subject Spectroscopy es_ES
dc.subject Prognosis es_ES
dc.subject Leukemia es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1017/S0269888911000129
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP6/027214/EU/Agent-based Distributed Decision Support System for brain tumour diagnosis and prognosis/HEALTHAGENTS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//PTQ-08-01-06802/ES/PTQ-08-01-06802/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MEC//PTQ05-02-03386/ES/PTQ05-02-03386/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//RD07%2F0067%2F2001/ES/RED TEMÁTICA DE INVESTIGACIÓN COOPERATIVA EN BIOMEDICINA COMPUTACIONAL/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Sáez Silvestre, C.; García Gómez, JM.; Vicente Robledo, J.; Tortajada Velert, S.; Luts, J.; Dupplaw, D.; Van Huffel, S.... (2011). A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents. Knowledge Engineering Review. 26(3):283-301. https://doi.org/10.1017/S0269888911000129 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1017/S0269888911000129 es_ES
dc.description.upvformatpinicio 283 es_ES
dc.description.upvformatpfin 301 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 26 es_ES
dc.description.issue 3 es_ES
dc.relation.senia 208965
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Educación y Ciencia
dc.contributor.funder Instituto de Salud Carlos III


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