Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical...

Mechanical ventilation is essential in intensive care units (ICUs) but poses risks such as ventilator-associated complications and high costs. The accuracy of predicting mechanical ventilation duration using clinical information is limited. Predicting ventilation duration accurately can aid clinical decisions like resource-allocation and early tracheostomy-planning.

To develop explainable artificial intelligence (AI) models for predicting mechanical ventilation duration leveraging diverse clinical parameters from ICU patient data.

This development and testing study analysed 323 mechanically ventilated patients {(n = 323, Male:Female = 160:163, Age = 42.87 ± 19.54 years (mean ± standard deviation)} from three ICUs at AIIMS, Delhi. The dataset included 100-clinical parameters per patient. Two models were developed: (1) A regression model (n = 323) to predict ventilation duration in days, and (2) A classification model (n = 218, non-tracheostomized) to predict short- (≤3 days) vs. long-term (>3 days) ventilation requirements. The misclassification-cost was altered for the classification model. Feature selection was performed using Shapley additive explanations (SHAP) on a random forest model, and training was done with 5-fold cross-validation (80% training, 20% testing).

The least-squares boosting regression model achieved root mean squared error (RMSE) of 4.66 days and coefficient of Determination (R²) of 0.65 using 34-SHAP-selected features, with tracheostomy (53.66% importance) being the top predictor. The best classification model, K-nearest neighbours, achieved 79.1% accuracy, Area under the receiver-operating-characteristic-curve (AUROC) of 0.82, sensitivity of 71.4%, and specificity of 86.4% using 47-SHAP-selected features. Key predictors included ICU admission type (8.1%), PO₂ (5.6%), and pH (5%).

AI-driven prediction of ventilation duration can enhance ICU workflows, optimize resource use, and improve personalized care. SHAP-based feature selection promotes AI interpretability, aiding clinical adoption.