Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients
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s41746-019-0192-z.pdf
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Author(s)
Dauvin, Antonin
Donado, Carolina
Bachtiger, Patrik
Huang, Ke-Chun
Sauer, Christopher Martin
Ramazotti, Daniele
Bonvini, Matteo
Celi, Leo Anthony G.
Douglas, Molly J.
Date Issued
November 29, 2019
Journal
NPJ Digital Medicine
Publisher
Springer Science+Business Media
Citation
Dauvin, Antonin et al. "Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients." NPJ Digital Medicine, 2, 1, (November 2019): 116.
Version
Final published version
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. Keywords: Acute kidney injury; Anaemia; Chronic kidney disease; Computational models; Data integration
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41746-019-0192-z
