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Cure models to estimate time until hospitalization due to COVID-19

dc.contributor.authorPedrosa-Laza, Maria
dc.contributor.authorLópez-Cheda, Ana
dc.contributor.authorCao, Ricardo
dc.date.accessioned2023-12-26T12:06:10Z
dc.date.available2023-12-26T12:06:10Z
dc.date.issued2022-01
dc.identifier.citationPedrosa-Laza, M., López-Cheda, A. & Cao, R. Cure models to estimate time until hospitalization due to COVID-19. Appl Intell 52, 794–807 (2022). https://doi.org/10.1007/s10489-021-02311-8es_ES
dc.identifier.issn1573-7497
dc.identifier.urihttp://hdl.handle.net/2183/34641
dc.descriptionThis version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10489-021-02311-8es_ES
dc.description.abstract[Abstract]: A short introduction to survival analysis and censored data is included in this paper. A thorough literature review in the field of cure models has been done. An overview on the most important and recent approaches on parametric, semiparametric and nonparametric mixture cure models is also included. The main nonparametric and semiparametric approaches were applied to a real time dataset of COVID-19 patients from the first weeks of the epidemic in Galicia (NW Spain). The aim is to model the elapsed time from diagnosis to hospital admission. The main conclusions, as well as the limitations of both the cure models and the dataset, are presented, illustrating the usefulness of cure models in this kind of studies, where the influence of age and sex on the time to hospital admission is shown.es_ES
dc.description.sponsorshipMPL activity was funded by the Science, Technology, and Innovation Plan of the Principality of Asturias (Spain) Ref: FC-GRUPIN-IDI/2018/000225, which is part-funded by the European Regional Development Fund (ERDF). ALC was sponsored by the BEATRIZ GALINDO JUNIOR Spanish Grant from MICINN (Ministerio de Ciencia, Innovación y Universidades) with reference BGP18/00154. RC and ALC acknowledge partial support by the MINECO grant MTM2017-82724-R, and by the Xunta de Galicia: Grupos de Referencia Competitiva ED431C-2020-14, Centro de Investigación del Sistema universitario de Galicia ED431G 2019/01, and Axencia Galega de Innovación (Ayudas proyectos de investigación COVID-19 presentados a la convocatoria del ISCIII IN845D 2020/26 - Programa Operativo FEDER Galicia 2014-2020), all of them through the ERDF.es_ES
dc.description.sponsorshipGobierno del Principado de Asturias; FC-GRUPIN-IDI/2018/000225es_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2020-14es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSIONes_ES
dc.relation.urihttps://doi.org/10.1007/s10489-021-02311-8es_ES
dc.rights© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021es_ES
dc.subjectCensored dataes_ES
dc.subjectCOVID-19es_ES
dc.subjectHospital demandes_ES
dc.subjectForecastinges_ES
dc.subjectSurvival analysises_ES
dc.titleCure models to estimate time until hospitalization due to COVID-19es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleApplied Intelligencees_ES
UDC.volume52es_ES
UDC.startPage794es_ES
UDC.endPage807es_ES


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