Publication:

Predicting time-to-intubation after critical care admission using machine learning and cured fraction information

Date

2024
Journal article
https://doi.org/10.1016/j.artmed.2024.102817
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dc.contributor.authorVenturini, Michela
dc.contributor.authorVan Keilegom, Ingrid
dc.contributor.authorDe Corte, Wouter
dc.contributor.authorVens, Celine
dc.contributor.imecauthorVenturini, Michela
dc.contributor.imecauthorVens, Celine
dc.contributor.orcidimecVens, Celine::0000-0003-0983-256X
dc.contributor.orcidimecVenturini, Michela::0000-0002-9947-0218
dc.date.accessioned2024-09-09T10:43:26Z
dc.date.available2024-04-05T18:17:47Z
dc.date.available2024-09-09T10:43:26Z
dc.date.issued2024
dc.description.wosFundingTextM.V. is funded through the Research Fund Flanders, Belgium (project G0A2120N). I.V.K. gratefully acknowledges funding from the FWO and F.R.S.-FNRS under the Excellence of Science (EOS) programme, Belgium, project ASTeRISK (grant No. 40007517). The authors also acknowledge the Flemish Government (AI Research Program), Belgium.
dc.identifier.doi10.1016/j.artmed.2024.102817
dc.identifier.issn0933-3657
dc.identifier.pmidMEDLINE:38553157
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/43783
dc.publisherELSEVIER
dc.source.beginpageArt. 102817
dc.source.endpageN/A
dc.source.issueApril
dc.source.journalARTIFICIAL INTELLIGENCE IN MEDICINE
dc.source.numberofpages14
dc.source.volume150
dc.subject.keywordsCARDIAC-ARREST
dc.subject.keywordsILL PATIENTS
dc.subject.keywordsSURVIVAL
dc.subject.keywordsMODELS
dc.subject.keywordsPNEUMONIA
dc.subject.keywordsOUTCOMES
dc.title

Predicting time-to-intubation after critical care admission using machine learning and cured fraction information

dc.typeJournal article
dspace.entity.typePublication
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