23: Machine Learning Approaches for Predicting In-Hospital Mortality in Drug Overdose Patients

Music City Center 

Machine learning (ML) algorithms are effective in predicting clinical outcomes. This study aimed to identify ML models with the best performance for predicting mortality and possibly improving patient outcomes in drug overdose care. This study included data on 1452 patients seen at Emergency Departments in East Texas (9/1/2021-12/31/2024) for overdose care. Forty features were selected for six ML models, including decision tree, gradient boosting, logistic regression, neural network, random forest, and support vector machine to predict in-hospital mortality. ML models were compared by the area under the receiver operating characteristics curve (AUC) and KS (Youden). The analysis revealed that the random forest model was the best with superior AUC and KS values. The five most crucial features in prediction across all models are the Glasgow coma scale, systolic blood pressure, BMI, age, and diastolic blood pressure at admission. The random forest model was the best-performing ML model, making it more reliable in predicting mortality with the potential to significantly impact clinical practice, underlining the importance of such research in predictive modeling in Addiction medicine.

Drug overdose death

predicting in-hospital mortality

machine learning algorithms 

Section on Statistics in Epidemiology