Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/45495
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dc.contributor.authorCeccarelli, Fulvia-
dc.contributor.authorSciandrone, Marco-
dc.contributor.authorPerricone, Carlo-
dc.contributor.authorGalvan, Giulio-
dc.contributor.authorMorelli, Francesco-
dc.contributor.authorVicente, Luís Nunes-
dc.contributor.authorLeccese, Ilaria-
dc.contributor.authorMassaro, Laura-
dc.contributor.authorCipriano, Enrica-
dc.contributor.authorSpinelli, Francesca Romana-
dc.contributor.authorAlessandri, Cristiano-
dc.contributor.authorValesini, Guido-
dc.contributor.authorConti, Fabrizio-
dc.date.accessioned2017-12-28T09:44:18Z-
dc.date.available2017-12-28T09:44:18Z-
dc.date.issued2017-
dc.identifier.urihttp://hdl.handle.net/10316/45495-
dc.description.abstractThe increased survival in Systemic Lupus Erythematosus (SLE) patients implies the development of chronic damage, occurring in up to 50% of cases. Its prevention is a major goal in the SLE management. We aimed at predicting chronic damage in a large monocentric SLE cohort by using neural networks.por
dc.language.isoengpor
dc.publisherMasataka Kuwana, Keio University, Japanpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147205/PTpor
dc.rightsopenAccesspor
dc.subjectAdultpor
dc.subjectDisease Progressionpor
dc.subjectFemalepor
dc.subjectHumanspor
dc.subjectLongitudinal Studiespor
dc.subjectLupus Erythematosus, Systemicpor
dc.subjectMachine Learningpor
dc.subjectMalepor
dc.subjectSensitivity and Specificitypor
dc.subjectSeverity of Illness Indexpor
dc.titlePrediction of chronic damage in systemic lupus erythematosus by using machine-learning modelspor
dc.typearticle-
degois.publication.firstPagee0174200por
degois.publication.issue3por
degois.publication.titlePloS onepor
dc.relation.publisherversionhttps://doi.org/10.1371/ journal.pone.0174200por
dc.peerreviewedyespor
dc.identifier.doi10.1371/journal.pone.0174200por
degois.publication.volume12por
item.fulltextCom Texto completo-
item.grantfulltextopen-
item.languageiso639-1en-
Appears in Collections:I&D CMUC - Artigos em Revistas Internacionais
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