Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.

ABSTRACT:

Background

Machine learning (ML) models are increasingly utilized to predict mortality in patients with sepsis-associated acute kidney injury (SA-AKI), frequently surpassing traditional scoring systems. Despite their efficacy, inconsistencies in model quality remain a concern. This review aims to evaluate existing ML-based SA-AKI mortality prediction models, with a focus on development quality, methodological rigor, and predictive performance.

Objective

To systematically assess ML-based mortality risk prediction models for SA-AKI patients.

Methods

A comprehensive literature search on ML-based SA-AKI mortality prediction models was conducted across PubMed, Cochrane, Embase, and Web of Science from the inception of these databases until July 2025. Two researchers indepe

SUBMITTER: Li X 

PROVIDER: S-EPMC12540499 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature