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In NPJ digital medicine ; h5-index 0.0

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.

Dauvin Antonin, Donado Carolina, Bachtiger Patrik, Huang Ke-Chun, Sauer Christopher Martin, Ramazzotti Daniele, Bonvini Matteo, Celi Leo Anthony, Douglas Molly J


Acute kidney injury, Anaemia, Chronic kidney disease, Computational models, Data integration