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Predicting Outcomes of Acute Kidney Injury in Critically Ill Patients Using Machine Learning by Fateme Nateghi Haredasht; Liesbeth Viaene; Hans Pottel; Wouter De Corte; Celine Vens is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Predicting Outcomes of Acute Kidney Injury in Critically Ill Patients Using Machine Learning about?

Acute Kidney Injury (AKI) is a sudden episode of kidney failure that is frequently seen in critically ill patients. AKI has been linked to chronic kidney disease (CKD) and mortality. We developed machine learning-based prediction models to predict outcomes following AKI stage 3 events in the intensive care unit. We conducted a prospective observational study that used the medical records of ICU patients diagnosed with AKI stage 3. A random forest algorithm was used to develop two models that can predict patients who will progress to CKD after three and six months of experiencing AKI stage 3. To predict mortality, two survival prediction models have been presented using random survival forests and survival XGBoost. We evaluated established CKD prediction models using AUCROC, and AUPR curves and compared them with the baseline logistic regression models. The mortality prediction models were evaluated with an external test set, and the C-indices were compared to baseline COXPH. We included 101 critically ill patients who experienced AKI stage 3. To increase the training set for the mortality prediction task, an unlabeled dataset has been added. The RF (AUPR: 0.895 and 0.848) and XGBoo

Who reads Predicting Outcomes of Acute Kidney Injury in Critically Ill Patients Using Machine Learning?

Author

Fateme Nateghi Haredasht; Liesbeth Viaene; Hans Pottel; Wouter De Corte; Celine Vens

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)