Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making.

The escalating therapeutic use of methadone has coincided with an increase in accidental ingestions, particularly among children ≤ 5 years. This study utilized machine learning (ML) methodologies on data from the National Poison Data System (NPDS) to predict pediatric methadone poisoning outcomes to enhance clinical decision-making. We analyzed 140 medical parameters from pediatric patient records. Pre-processing steps, including synthetic oversampling, addressed the imbalanced distribution of the outcome variable. We evaluated various ML models in multiclass classification tasks. Random forest showed versatility with an accuracy of 0.96 and a strong receiver operating characteristic area under the curve (ROC AUC) (0.98). Meanwhile, the support vector machine (SVM) had the highest negative predictive value (NPV) (0.64). Shapley Additive exPlanation (SHAP) analysis identified key predictors such as coma, cyanosis, respiratory arrest, and respiratory depression for predicting serious outcomes.

conclusionThis research emphasizes the utility of ML in clinical settings for early detection and intervention in methadone poisoning events in children, highlighting the synergy between data science and clinical expertise. WHAT IS KNOWN: • The increased use of methadone for treatment has been associated with a rise in accidental ingestions, particularly in children under five years old. • Methadone poisoning in young children can lead to severe outcomes, including respiratory depression and coma, requiring urgent medical intervention. WHAT IS NEW: • Machine learning models, particularly Random Forest and Bagging, outperform traditional methods in predicting methadone poisoning outcomes in children. • SHAP analysis provides novel insights into key predictors of severe outcomes, enabling improved clinical decision-making and risk stratification.