Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients
dc.contributor.author | Dauvin, Antonin | |
dc.contributor.author | Donado, Carolina | |
dc.contributor.author | Bachtiger, Patrik | |
dc.contributor.author | Huang, Ke-Chun | |
dc.contributor.author | Sauer, Christopher Martin | |
dc.contributor.author | Ramazzotti, Daniele | |
dc.contributor.author | Bonvini, Matteo | |
dc.contributor.author | Celi, Leo Anthony | |
dc.contributor.author | Douglas, Molly J | |
dc.date.accessioned | 2020-01-31T20:18:37Z | |
dc.date.available | 2020-01-31T20:18:37Z | |
dc.date.issued | 2019-11-29 | |
dc.identifier.citation | Dauvin, 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 | en_US |
dc.identifier.issn | 2398-6352 | |
dc.identifier.pmid | 31815192 | |
dc.identifier.doi | 10.1038/s41746-019-0192-z | |
dc.identifier.uri | http://hdl.handle.net/10150/636809 | |
dc.description.abstract | Patients 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.iso | en | en_US |
dc.publisher | NATURE PUBLISHING GROUP | en_US |
dc.rights | Copyright © The Author(s) 2019. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License. | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Acute kidney injury | en_US |
dc.subject | Anaemia | en_US |
dc.subject | Chronic kidney disease | en_US |
dc.subject | Computational models | en_US |
dc.subject | Data integration | en_US |
dc.title | Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients | en_US |
dc.type | Article | en_US |
dc.contributor.department | Univ Arizona, Coll Med | en_US |
dc.identifier.journal | NPJ DIGITAL MEDICINE | en_US |
dc.description.note | Open access journal | en_US |
dc.description.collectioninformation | This 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.version | Final published version | en_US |
dc.source.journaltitle | NPJ digital medicine | |
refterms.dateFOA | 2020-01-31T20:18:38Z |