Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach.

ABSTRACT:

Background

Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and extended hospital stays. Early detection of kidney injury is crucial for improving outcomes. This study presents a machine learning-based risk prediction model for AKI and AKD in pediatric patients, enabling personalized risk predictions.

Methods

Data from 2,346 hospitalized pediatric patients, collected between January 2020 and January 2023, were divided into an 85% training set and a 15% test set. Predictive models were constructed using eight machine learning algorithms and two ensemble algorithms, with the optimal model identified through AUROC. SHAP was used to interpret the model, and an online prediction tool was developed

SUBMITTER: Xu L 

PROVIDER: S-EPMC11648138 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature