Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.

ABSTRACT:

Background

Acute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines.

Methods

EHR data from 16,863 pediatric critical care patients between 1 month to 21 years of age from three independent institutions were used to develop a single machine learning model for early prediction of creatinine-based AKI using intelligently engineered predicto

SUBMITTER: Dong J 

PROVIDER: S-EPMC8353807 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature