A deep learning-based dynamic model for predicting acute kidney injury risk severity in postoperative patients.

ABSTRACT:

Background

Acute kidney injury is a common postoperative complication affecting between 10% and 30% of surgical patients. Acute kidney injury is associated with increased resource usage and chronic kidney disease development, with more severe acute kidney injury suggesting more aggressive deterioration in clinical outcomes and mortality.

Methods

We considered 42,906 surgical patients admitted to University of Florida Health (n = 51,806) between 2014 and 2021. Acute kidney injury stages were determined using the Kidney Disease Improving Global Outcomes serum creatinine criteria. We developed a recurrent neural network-based model to continuously predict acute kidney injury risk and state in the following 24 hours and compared it with logistic regression, random forest, and mu

SUBMITTER: Adiyeke E 

PROVIDER: S-EPMC10683578 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature