Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS)

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.

CONCLUSION

This 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.

Prediction of acute methanol poisoning prognosis using machine learning techniques

Methanol poisoning is a global public health concern, especially prevalent in developing nations. This study focuses on predicting the severity of methanol intoxication using machine learning techniques, aiming to improve early identification and prognosis assessment. The study, conducted at Loghman Hakim Hospital in Tehran, Iran. The data pertaining to individuals afflicted with methanol poisoning was retrieved retrospectively and divided into training and test groups at a ratio of 70:30. The selected features were then inputted into various machine learning methods. The models were implemented using the Scikit-learn library in the Python programming language. Ultimately, the efficacy of the developed models was assessed through ten-fold cross-validation techniques and specific evaluation criteria, with a confidence level of 95%. A total number of 897 patients were included and divided in three groups including without sequel (n = 573), with sequel (n = 234), and patients who died (n = 90). The two-step feature selection was yielded 43 features in first step and 23 features in second step. In best model (Gradient Boosting Classifier) test dataset metric by 32 features younger age, higher methanol ingestion, respiratory symptoms, lower GCS scores, type of visual symptom, duration of therapeutic intervention, ICU admission, and elevated CPK levels were among the most important features predicting the prognosis of methanol poisoning. The Gradient Boosting Classifier demonstrated the highest predictive capability, achieving AUC values of 0.947 and 0.943 in the test dataset with 43 and 23 features, respectively. This research introduces a machine learning-driven prognostic model for methanol poisoning, demonstrating superior predictive capabilities compared to traditional statistical methods. The identified features provide valuable insights for early intervention and personalized treatment strategies.