Interpretable machine learning models for early prediction of acute kidney injury after cardiac surgery.

ABSTRACT:

Objective

Postoperative acute kidney injury (PO-AKI) is a common complication after cardiac surgery. We aimed to evaluate whether machine learning algorithms could significantly improve the risk prediction of PO-AKI.

Methods

The retrospective cohort study included 2310 adult patients undergoing cardiac surgery in a tertiary teaching hospital, China. Postoperative AKI and severe AKI were identified by the modified KDIGO definition. The sample was randomly divided into a derivation set and a validation set based on a ratio of 4:1. Exploiting conventional logistic regression (LR) and five ML algorithms including decision tree, random forest, gradient boosting classifier (GBC), Gaussian Naive Bayes and multilayer perceptron, we developed and validated the prediction models of PO

SUBMITTER: Jiang J 

PROVIDER: S-EPMC10631004 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature