Predicting acute kidney injury with an artificial intelligence-driven model in a pediatric cardiac intensive care unit.

ABSTRACT:

Background

Acute kidney injury (AKI) is among the most common complications following cardiac surgery in adult and pediatric patients, significantly affecting morbidity and mortality. Artificial Intelligence (AI) with Machine Learning (ML) can be used to predict outcomes. AKI diagnosis anticipation may be an ideal target of these methods. The scope of the study is building a Machine Learning (ML) train model with Random Forest (RF) algorithm, based on electronic health record (EHR) data, able to forecast AKI continuously after 48 h in post-cardiac surgery children, and to test its performance. Four hundred nineteen consecutive patients out of 1115 hospital admissions were enrolled in a single-center retrospective study. Patients were younger than 18 years and admitted from August 2

SUBMITTER: Fragasso T 

PROVIDER: S-EPMC10583404 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature