Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients
Sauer, Christopher Martin
Celi, Leo Anthony
Douglas, Molly J
AffiliationUniv Arizona, Coll Med
MetadataShow full item record
PublisherNATURE PUBLISHING GROUP
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-z
JournalNPJ DIGITAL MEDICINE
RightsCopyright © The Author(s) 2019. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License.
Collection InformationThis 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 firstname.lastname@example.org.
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.
NoteOpen access journal
VersionFinal published version
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.
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