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dc.contributor.authorDauvin, Antonin
dc.contributor.authorDonado, Carolina
dc.contributor.authorBachtiger, Patrik
dc.contributor.authorHuang, Ke-Chun
dc.contributor.authorSauer, Christopher Martin
dc.contributor.authorRamazzotti, Daniele
dc.contributor.authorBonvini, Matteo
dc.contributor.authorCeli, Leo Anthony
dc.contributor.authorDouglas, Molly J
dc.date.accessioned2020-01-31T20:18:37Z
dc.date.available2020-01-31T20:18:37Z
dc.date.issued2019-11-29
dc.identifier.citationDauvin, A., Donado, C., Bachtiger, P. et al. Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients. npj Digit. Med. 2, 116 (2019). https://doi.org/10.1038/s41746-019-0192-zen_US
dc.identifier.issn2398-6352
dc.identifier.pmid31815192
dc.identifier.doi10.1038/s41746-019-0192-z
dc.identifier.urihttp://hdl.handle.net/10150/636809
dc.description.abstractPatients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86–0.89) classify an individual patient’s baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.en_US
dc.language.isoenen_US
dc.publisherNATURE PUBLISHING GROUPen_US
dc.rightsCopyright © The Author(s) 2019. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAcute kidney injuryen_US
dc.subjectAnaemiaen_US
dc.subjectChronic kidney diseaseen_US
dc.subjectComputational modelsen_US
dc.subjectData integrationen_US
dc.titleMachine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patientsen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Coll Meden_US
dc.identifier.journalNPJ DIGITAL MEDICINEen_US
dc.description.noteOpen access journalen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleNPJ digital medicine
refterms.dateFOA2020-01-31T20:18:38Z


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Copyright © The Author(s) 2019. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License.
Except where otherwise noted, this item's license is described as Copyright © The Author(s) 2019. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License.