Prediction of acute kidney injury in patients with femoral neck fracture utilizing machine learning.

ABSTRACT:

Background

Acute kidney injury (AKI) is a common complication associated with significant morbidity and mortality in high-energy trauma patients. Given the poor efficacy of interventions after AKI development, it is important to predict AKI before its diagnosis. Therefore, this study aimed to develop models using machine learning algorithms to predict the risk of AKI in patients with femoral neck fractures.

Methods

We developed machine-learning models using the Medical Information Mart from Intensive Care (MIMIC)-IV database. AKI was predicted using 10 predictive models in three-time windows, 24, 48, and 72 h. Three optimal models were selected according to the accuracy and area under the receiver operating characteristic curve (AUROC), and the hyperparameters were adjusted

SUBMITTER: Liu J 

PROVIDER: S-EPMC9360500 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature