Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis.

ABSTRACT:

Background

Predicting mortality in sepsis-related acute kidney injury facilitates early data-driven treatment decisions. Machine learning is predicting mortality in S-AKI in a growing number of studies. Therefore, we conducted this systematic review and meta-analysis to investigate the predictive value of machine learning for mortality in patients with septic acute kidney injury.

Methods

The PubMed, Web of Science, Cochrane Library and Embase databases were searched up to 20 July 2024 This was supplemented by a manual search of study references and review articles. Data were analysed using STATA 14.0 software. The risk of bias in the prediction model was assessed using the Predictive Model Risk of Bias Assessment Tool.

Results

A total of 8 studies were included, with

SUBMITTER: Lv X 

PROVIDER: S-EPMC11663330 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature