A data-driven model for early prediction of need for invasive mechanical ventilation in pediatric intensive care unit patients.

ABSTRACT:

Rationale

Acute respiratory failure is a life-threatening clinical outcome in critically ill pediatric patients. In severe cases, patients can require mechanical ventilation (MV) for survival. Early recognition of these patients can potentially help clinicians alter the clinical course and lead to improved outcomes.

Objectives

To build a data-driven model for early prediction of the need for mechanical ventilation in pediatric intensive care unit (PICU) patients.

Methods

The study consists of a single-center retrospective observational study on a cohort of 13,651 PICU patients admitted between 1/01/2010 and 5/15/2018 with a prevalence of 8.06% for MV due to respiratory failure. XGBoost (extreme gradient boosting) and a convolutional neural network (CNN) using medicati

SUBMITTER: Bose SN 

PROVIDER: S-EPMC10403092 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature