Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy.

ABSTRACT: BACKGROUND:Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing continuous renal replacement therapy (CRRT) for severe acute kidney injury. Accordingly, the present study applies machine learning algorithms to improve prediction accuracy for this patient subset. METHODS:We randomly divided a total of 1571 adult patients who started CRRT for acute kidney injury into training (70%, n = 1094) and test (30%, n = 477) sets. The primary output consisted of the probability of mortality during admission to the intensive care unit (ICU) or hospital. We compared the area under the receiver operating characteristic curve

SUBMITTER: Kang MW 

PROVIDER: S-EPMC7006166 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature