Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters.

ABSTRACT:

Background

To protect patients during anesthesia, difficult airway management is a serious issue that needs to be carefully planned for and carried out. Machine learning prediction tools have recently become increasingly common in medicine, frequently surpassing more established techniques. This study aims to utilize machine learning techniques on predictive parameters for challenging airway management.

Methods

This study was cross-sectional. The Shahid Beheshti University of Medical Sciences in Iran's Loghman Hakim and Shahid Labbafinezhad hospitals provided 622 records in total for analysis. Using the forest of trees approach and feature importance, important features were chosen. The Synthetic Minority Oversampling Technique (SMOTE) and repeated edited nearest neighbor un

SUBMITTER: Sezari P 

PROVIDER: S-EPMC11654375 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature