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dc.creatorGonzález Abril, Luises
dc.creatorAngulo, Cecilioes
dc.creatorOrtega Ramírez, Juan Antonioes
dc.creatorLópez Guerra, José Luises
dc.date.accessioned2022-08-09T08:51:25Z
dc.date.available2022-08-09T08:51:25Z
dc.date.issued2021
dc.identifier.citationGonzález Abril, L., Angulo, C., Ortega Ramírez, J.A. y López Guerra, J.L. (2021). Generative adversarial networks for anonymized healthcare of lung cancer patients. Electronics, 10 (18), 2220.
dc.identifier.issn2079-9292es
dc.identifier.urihttps://hdl.handle.net/11441/136067
dc.description.abstractThe digital twin in health care is the dynamic digital representation of the patient’s anatomy and physiology through computational models which are continuously updated from clinical data. Furthermore, used in combination with machine learning technologies, it should help doctors in therapeutic path and in minimally invasive intervention procedures. Confidentiality of medical records is a very delicate issue, therefore some anonymization process is mandatory in order to maintain patients privacy. Moreover, data availability is very limited in some health domains like lung cancer treatment. Hence, generation of synthetic data conformed to real data would solve this issue. In this paper, the use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients is introduced as a tool to solve this problem in the form of anonymized synthetic patients. Generated synthetic patients are validated using both statistical methods, as well as by oncologists using the indirect mortality rate obtained for patients in different stages.es
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades PGC2018-102145-B-C21es
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades PGC2018-102145-B-C22es
dc.description.sponsorshipUnión Europea 825619 (AI4EU)es
dc.formatapplication/pdfes
dc.format.extent17 p.es
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofElectronics, 10 (18), 2220.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDigital twines
dc.subjectAnonymizationes
dc.subjectGenerative adversarial networkes
dc.subjectLung canceres
dc.titleGenerative adversarial networks for anonymized healthcare of lung cancer patientses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Economía Aplicada Ies
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDPGC2018-102145-B-C21es
dc.relation.projectIDPGC2018-102145-B-C22es
dc.relation.projectID825619 (AI4EU)es
dc.relation.publisherversionhttps://doi.org/10.3390/electronics10182220es
dc.identifier.doi10.3390/electronics10182220es
dc.journaltitleElectronicses
dc.publication.volumen10es
dc.publication.issue18es
dc.publication.initialPage2220es
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidadeses
dc.contributor.funderUnión Europeaes

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