Machine learning prediction model for 28-day mortality among hepatic failure patients complicated by acute respiratory distress syndrome.

ABSTRACT:

Objectives

Hepatic failure is a common and severe condition among intensive care unit (ICU) patients. Its complication with acute respiratory distress syndrome (ARDS) is consistently associated with poor clinical outcomes and a significant disease burden. Early identification of high-risk patients is essential for improving clinical outcomes. This study aimed to develop and validate a machine learning (ML) model to predict 28-day mortality in ICU patients with hepatic failure complicated by ARDS.

Methods

Data were extracted from the Medical Information Mart for Intensive Care IV database, focusing on patients with hepatic failure complicated by ARDS. The cohort was randomly divided into an 80% training set and a 20% validation set. Six ML algorithms were applied to analyze c

SUBMITTER: Yi W 

PROVIDER: S-EPMC12722651 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature