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Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the
Omid Mehrpour1, Samaneh Nakhaee2, Jafar Abdollahi3
1Michigan Poison & Drug Information Center, School of Medicine, Wayne State University, Detroit, MI, USA. Omid.mehrpour@yahoo.com.au.
Insights
Machine learning accurately predicts pediatric methadone poisoning outcomes, identifying key predictors like coma and respiratory depression. This aids early clinical intervention for child safety.
Area of Science:
- Pediatric Toxicology
- Clinical Data Science
- Machine Learning in Healthcare
Background:
- Increased therapeutic methadone use correlates with a rise in accidental pediatric ingestions.
- Pediatric methadone poisoning can cause severe outcomes such as respiratory depression and coma, necessitating prompt medical attention.
Purpose of the Study:
- To apply machine learning (ML) methodologies to predict pediatric methadone poisoning outcomes.
- To enhance clinical decision-making for accidental methadone ingestions in children.
- To identify key predictors of severe outcomes in pediatric methadone poisoning.
Main Methods:
- Utilized the National Poison Data System (NPDS) database, analyzing 140 medical parameters from pediatric patient records.
- Employed pre-processing techniques, including synthetic oversampling, to manage imbalanced outcome data.
- Evaluated multiple ML models for multiclass classification, including Random Forest and Support Vector Machine (SVM).
Main Results:
- Random Forest achieved high accuracy (0.96) and ROC AUC (0.98) in predicting poisoning outcomes.
- Support Vector Machine (SVM) demonstrated the highest Negative Predictive Value (NPV) of 0.64.
- Shapley Additive Explanation (SHAP) analysis identified coma, cyanosis, respiratory arrest, and respiratory depression as critical predictors of severe outcomes.
Conclusions:
- Machine learning models are effective tools for early detection and intervention in pediatric methadone poisoning.
- This study highlights the value of integrating data science with clinical expertise for improved patient outcomes.
- ML-driven insights enhance risk stratification and clinical decision-making in pediatric toxicology.
Abstract:
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.
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