Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.

ABSTRACT:

Background

With the development of artificial intelligence, the application of machine learning to develop predictive models for sepsis-associated acute kidney injury has made potential breakthroughs in early identification, grading, diagnosis, and prognosis determination.

Methods

Here, we conducted a systematic search of the PubMed, Cochrane Library, Embase (Ovid), Web of Science, and Scopus databases on April 28, 2023, and screened relevant literature. Then, we comprehensively extracted relevant data related to machine learning algorithms, predictors, and predicted objectives. We subsequently performed a critical evaluation of research quality, data aggregation, and analyses.

Results

We screened 25 studies on predictive models for sepsis-associated acute kidney inju

SUBMITTER: Li J 

PROVIDER: S-EPMC11293267 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature