Publication:

Predicting outcomes of acute kidney injury in critically ill patients using machine learning

Date

2023
Journal article
https://doi.org/10.1038/s41598-023-36782-1
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dc.contributor.authorNateghi, Fateme
dc.contributor.authorViaene, Liesbeth
dc.contributor.authorPottel, Hans
dc.contributor.authorDe Corte, Wouter
dc.contributor.authorVens, Celine
dc.contributor.imecauthorNateghi, Fateme
dc.contributor.imecauthorVens, Celine
dc.contributor.orcidimecNateghi, Fateme::0000-0002-8874-8835
dc.contributor.orcidimecVens, Celine::0000-0003-0983-256X
dc.date.accessioned2024-04-25T11:52:04Z
dc.date.available2023-07-04T20:27:21Z
dc.date.available2024-04-25T11:52:04Z
dc.date.embargo2023-06-18
dc.date.issued2023
dc.description.wosFundingTextThis work was supported by KU Leuven Internal Funds (grant 3M180314).
dc.identifier.doi10.1038/s41598-023-36782-1
dc.identifier.issn2045-2322
dc.identifier.pmidMEDLINE:37331979
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/42124
dc.publisherNATURE PORTFOLIO
dc.source.beginpageArt. 9864
dc.source.endpageN/A
dc.source.issue1
dc.source.journalSCIENTIFIC REPORTS
dc.source.numberofpages13
dc.source.volume13
dc.title

Predicting outcomes of acute kidney injury in critically ill patients using machine learning

dc.typeJournal article
dspace.entity.typePublication
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