Prediction of extubation failure among low birthweight neonates using machine learning.

ABSTRACT:

Objective

To develop machine learning models predicting extubation failure in low birthweight neonates using large amounts of clinical data.

Study design

Retrospective cohort study using MIMIC-III, a large single-center, open-source clinical dataset. Logistic regression and boosted-tree (XGBoost) models using demographics, medications, and vital sign and ventilatory data were developed to predict extubation failure, defined as reintubation within 7 days.

Results

1348 low birthweight (≤2500 g) neonates who received mechanical ventilation within the first 7 days were included, of which 350 (26%) failed a trial of extubation. The best-performing model was a boosted-tree model incorporating demographics, vital signs, ventilator parameters, and medications (AUROC 0.82). Th

SUBMITTER: Natarajan A 

PROVIDER: S-EPMC10348822 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature