Development and validation of a machine-learning model for prediction of hypoxemia after extubation in intensive care units.

ABSTRACT:

Background

Extubation is the process of removing tracheal tubes so that patients maintain oxygenation while they start to breathe spontaneously. However, hypoxemia after extubation is an important issue for critical care doctors and is associated with patients' oxygenation, circulation, recovery, and incidence of postoperative complications. Accuracy and specificity of most related conventional models remain unsatisfactory. We conducted a predictive analysis based on a supervised machine-learning algorithm for the precise prediction of hypoxemia after extubation in intensive care units (ICUs).

Methods

Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database for patients over age 18 who underwent mechanical ventilation in the ICU. The prima

SUBMITTER: Xia M 

PROVIDER: S-EPMC9201189 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature