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dc.contributor.authorGonzález Abril, Luis
dc.contributor.authorAngulo Bahón, Cecilio
dc.contributor.authorOrtega Ramírez, Juan Antonio
dc.contributor.authorLópez Guerra, José Luis
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2021-10-25T10:23:07Z
dc.date.available2021-10-25T10:23:07Z
dc.date.issued2021-09-01
dc.identifier.citationGonzález Abril, L. [et al.]. Generative adversarial networks for anonymized healthcare of lung cancer patients. "Electronics (Switzerland)", 1 Setembre 2021, vol. 10, núm. 18, p. 2220:1-2220:17.
dc.identifier.issn20799292
dc.identifier.urihttp://hdl.handle.net/2117/354398
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.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivatives International 4.0
dc.rights©2021. MDPI
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshMachine learning
dc.subject.lcshLungs--Cancer
dc.subject.lcshArtificial intelligence
dc.subject.otherDigital twin
dc.subject.otherAnonymization
dc.subject.otherGenerative adversarial network
dc.subject.otherLung cancer
dc.titleGenerative adversarial networks for anonymized healthcare of lung cancer patients
dc.typeArticle
dc.subject.lemacXarxes neuronals (Informàtica) -- Aplicacions
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacPulmons -- Càncer
dc.subject.lemacIntel·ligència artificial
dc.contributor.groupUniversitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement
dc.identifier.doi10.3390/electronics10182220
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/2079-9292/10/18/2220
dc.rights.accessOpen Access
local.identifier.drac32083043
dc.description.versionPostprint (published version)
local.citation.authorGonzález Abril, Luis; Angulo, C.; Ortega Ramírez, Juan Antonio; López, J.
local.citation.publicationNameElectronics (Switzerland)
local.citation.volume10
local.citation.number18
local.citation.startingPage2220:1
local.citation.endingPage2220:17


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