Machine Learning Predictions and Identifying Key Predictors for Safer Intubation: A Study on Video Laryngoscopy Views.

ABSTRACT: This study develops a predictive model for video laryngoscopic views using advanced machine learning techniques, aiming to enhance airway management's efficiency and safety. A total of 212 participants were involved, with 169 in the training set and 43 in the test set. We assessed outcomes using the percentage of glottic opening (POGO) score and considered factors like the modified Mallampati classification, thyromental height and distance, sternomental distance, mouth opening distance, and neck circumference. A range of machine learning algorithms was employed for data analysis, including Random Forest, Light Gradient Boosting Machine, K-Nearest Neighbors, Support Vector Regression, Ridge Regression, and Lasso Regression. The models' performance was evaluated on the test set, with Root Me

SUBMITTER: Kim JH 

PROVIDER: S-EPMC11433239 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature