Early prediction of ventilator-associated pneumonia in critical care patients: a machine learning model.

ABSTRACT:

Background

This study was performed to develop and validate machine learning models for early detection of ventilator-associated pneumonia (VAP) 24 h before diagnosis, so that VAP patients can receive early intervention and reduce the occurrence of complications.

Patients and methods

This study was based on the MIMIC-III dataset, which was a retrospective cohort. The random forest algorithm was applied to construct a base classifier, and the area under the receiver operating characteristic curve (AUC), sensitivity and specificity of the prediction model were evaluated. Furthermore, We also compare the performance of Clinical Pulmonary Infection Score (CPIS)-based model (threshold value ≥ 3) using the same training and test data sets.

Results

In total, 38,515 ventilati

SUBMITTER: Liang Y 

PROVIDER: S-EPMC9233772 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature