Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal.

ABSTRACT:

Background

The number of studies applying machine learning (ML) to predict acute kidney injury (AKI) has grown steadily over the past decade. We assess and critically appraise the state of the art in ML models for AKI prediction, considering performance, methodological soundness, and applicability.

Methods

We searched PubMed and ArXiv, extracted data, and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD), Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines.

Results

Forty-six studies from 3166 titles were included. Thirty-eight studies dev

SUBMITTER: Vagliano I 

PROVIDER: S-EPMC9664575 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature