Prediction of endotracheal tube size in pediatric patients: Development and validation of machine learning models.

ABSTRACT:

Objective

We aimed to construct and validate machine learning models for endotracheal tube (ETT) size prediction in pediatric patients.

Methods

Data of 990 pediatric patients underwent endotracheal intubation were retrospectively collected between November 2019 and October 2021, and separated into cuffed and uncuffed endotracheal tube subgroups. Six machine learning algorithms, including support vector regression (SVR), logistic regression (LR), random forest (RF), gradient boosting tree (GBR), decision tree (DTR) and extreme gradient boosting tree (XGBR), were selected to construct and validate models using ten-fold cross validation in training set. The optimal models were selected, and the performance were compared with traditional predictive formulas and clinicians. Furth

SUBMITTER: Zhou M 

PROVIDER: S-EPMC9631215 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature