Predicting length of stay in the pediatric intensive care unit at a tertiary center in Saudi Arabia using machine learning.

BACKGROUND: Prolonged stay in pediatric intensive care units (PICUs) is associated with increased mortality risk, elevated healthcare costs, and diminished critical care capacity. Accurate early prediction of length of stay (LOS) may facilitate resource allocation, discharge planning, and family counseling. Traditional regression-based models have demonstrated limited performance because of the complex, non-linear nature of pediatric critical illnesses. OBJECTIVES: To develop and internally validate machine-learning models that predict PICU LOS using admission-time clinical data and to identify key predictors using explainable artificial intelligence techniques. METHODS: This retrospective cohort study included all eligible PICU admissions at a tertiary center in Saudi Arabia (2013-2022). LOS was categorized into short, intermediate, and prolonged stay using percentile-based binning. Multiple supervised machine learning algorithms were trained on a stratified set with cross-validation hyperparameter tuning and evaluated on an independent held-out test set. Performance was assessed using accuracy and micro-averaged multiclass area under the curve and interpretability via SHapley Additive exPlanations. RESULTS: Data from 6,090 admissions were analyzed. The Light Gradient Boosting Machine and Categorical Boosting models demonstrated the best performance, achieving micro-averaged multiclass areas under the curve of 0.826 and 0.832, respectively. Discrimination was strongest for short- and prolonged-stay categories, with lower performance for intermediate stays. Key predictors of prolonged stay included early mechanical ventilation, admission source, post-operative status, physiological instability, and comorbidity burden. CONCLUSIONS: Machine-learning models using admission-time data can reliably classify PICU LOS, particularly at the extremes of stay duration. This explainable, data-driven approach may support early risk stratification and inform operational decision-making in pediatric critical care.