Cohort selection for clinical trials using deep learning models

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Show simple item record Segura-Bedmar, Isabel Ráez, Pablo 2020-10-21T10:28:12Z 2020-10-21T10:28:12Z 2019-11-01
dc.identifier.bibliographicCitation Segura-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.
dc.identifier.issn 1067-5027
dc.description.abstract The 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.
dc.description.sponsorship This 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).
dc.language.iso eng
dc.publisher Oxford University Press
dc.rights Copyright © The Author(s) 2019.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.subject.other Cohort selection
dc.subject.other Convolutional neural network
dc.subject.other Deep learning
dc.subject.other Multilabel text classification
dc.subject.other Recurrent neural network
dc.title Cohort selection for clinical trials using deep learning models
dc.type article
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2017-87548-C2-1-R
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1181
dc.identifier.publicationissue 11
dc.identifier.publicationlastpage 1188
dc.identifier.publicationvolume 26
dc.identifier.uxxi AR/0000023974
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.affiliation.dpto UC3M. Departamento de Informática
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)
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