Developing an explainable machine learning model to predict the mechanical ventilation duration of patients with ARDS in intensive care units.

ABSTRACT:

Background

Acute respiratory distress syndrome (ARDS) is common in intensive care units with high mortality rate and mechanical ventilation (MV) is the most important related treatment. Early prediction of MV duration has benefit for patients risk stratification and care strategies support.

Objective

To develop an explainable model for predicting mechanical ventilation (MV) duration in patients with ARDS using the machine learning (ML) approach.

Method

The number of 1,148, 1,697, and 29 ARDS patients admitted to intensive care units (ICU) in the MIMIC-IV, eICU-CRD, and AmsterdamUMCdb databases were included in the study. Features at MV initiation from the MIMIC-IV dataset were used to train prediction models based on seven supervised machine learning algorithms. After

SUBMITTER: Wang Z 

PROVIDER: S-EPMC9678346 | biostudies-literature | 2023 Mar-Apr

REPOSITORIES: biostudies-literature