Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization.

ABSTRACT:

Background

Sepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU).

Methods

A total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical

SUBMITTER: Zhou H 

PROVIDER: S-EPMC10106833 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature