Publication:
Cohort selection for clinical trials using deep learning models

dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)es
dc.contributor.authorSegura-Bedmar, Isabel
dc.contributor.authorRáez, Pablo
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2020-10-21T10:28:12Z
dc.date.available2020-10-21T10:28:12Z
dc.date.issued2019-11-01
dc.description.abstractThe goal of the 2018 n2c2 shared task on cohort selection for clinical trials (track 1) is to identify which patients meet the selection criteria for clinical trials. Cohort selection is a particularly demanding task to which natural language processing and deep learning can make a valuable contribution. Our goal is to evaluate several deep learning architectures to deal with this task.en
dc.description.sponsorshipThis work was supported by the Research Program of the Ministry of Economy and Competitiveness, Government of Spain, grant number TIN2017-87548-C2-1-R (DeepEMR project).en
dc.identifier.bibliographicCitationSegura-Bedmar I, Raez P. Cohort selection for clinical trials using deep learning models. J Am Med Inform Assoc. 2019 Nov 1;26(11):1181-1188.en
dc.identifier.doihttps://doi.org/10.1093/jamia/ocz139
dc.identifier.issn1067-5027
dc.identifier.publicationfirstpage1181
dc.identifier.publicationissue11
dc.identifier.publicationlastpage1188
dc.identifier.publicationtitleJOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATIONen
dc.identifier.publicationvolume26
dc.identifier.urihttps://hdl.handle.net/10016/31254
dc.identifier.uxxiAR/0000023974
dc.language.isoenges
dc.publisherOxford University Pressen
dc.relation.projectIDGobierno de España. TIN2017-87548-C2-1-Res
dc.rightsCopyright © The Author(s) 2019.es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.otherCohort selectionen
dc.subject.otherConvolutional neural networken
dc.subject.otherDeep learningen
dc.subject.otherMultilabel text classificationen
dc.subject.otherRecurrent neural networken
dc.titleCohort selection for clinical trials using deep learning modelsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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