Predicting Unplanned Intubations in Rib Fracture Patients: An Interpretable Machine Learning Approach.

ABSTRACT: BackgroundTraumatic rib fractures can lead to respiratory complications necessitating unplanned intubation, but predictors have been inadequately delineated. We used interpretable machine learning to predict unplanned intubations in rib fracture patients while identifying predictors.MethodsTQIP 2017-2022 was queried for adult patients admitted to the hospital following a rib fracture injury. An XGBoost model was developed to predict unplanned intubation using variables that can be known on admission. A 70/10/20 train/validation/test split was used. SHapley Additive exPlanations (SHAP) were used for interpretation. SHAP allows individualized interpretation of predictors for each patient.ResultsThe cohort had 905 615 patients; 2.3% had unplanned intubations. Model metrics at the F1 maximizin

SUBMITTER: Harry SC 

PROVIDER: S-EPMC12381933 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature