An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia.

ABSTRACT:

Background

Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significantly associated with poor outcomes. However, there is currently a lack of reliable and interpretable tools for assessing the risk of in-hospital mortality in VAP patients. This study aims to develop an interpretable machine learning (ML) prediction model to enhance the assessment of in-hospital mortality risk in VAP patients.

Methods

This study extracted VAP patient data from versions 2.2 and 3.1 of the MIMIC-IV database, using version 2.2 for model training and validation, and version 3.1 for external testing. Feature selection was conducted using the Boruta algorithm, and 14 ML models were constructed. The optimal model was identified based on the area under the receiver oper

SUBMITTER: Wei J 

PROVIDER: S-EPMC11706384 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature