Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making - Shivi Mendiratta; Vinay Gandhi Mukkelli; Esha Baidya Kayal; Puneet Khanna; Amit Mehndiratta (Digital Health)
Background 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. Objective To develop explainable artificial intelligence (AI) models for predicting mechanical ventilation duration leveraging diverse clinical parameters from ICU patient data. Methodology 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). Results 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%), PO 2 (5.6%), and pH (5%). Conclusion 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.