Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury.

ABSTRACT:

Objectives

This study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning algorithms.

Methods

Patients who met the criteria for inclusion were identified in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided according to the validation (n = 2440) and development (n = 9756, 80%) queues. Ensemble stepwise feature selection method was used to screen for effective features. The prediction models of short-term mortality were developed by seven machine learning algorithms. Ten-fold cross-validation was used to verify the performance of the algorithm in the development queue. The area under the receiver operating ch

SUBMITTER: Gao T 

PROVIDER: S-EPMC10878338 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature