A Machine Learning Approach to Predict Length of Stay for Opioid Overdose Admitted Patients

People are prone to develop opioid dependence and other health problems due to regular non-medical use, prolonged use, and misuse of opioids. The number of hospital admissions for opioid dependence is growing across the US. The length of stay (LOS) is an essential indicator that assesses the severity of opioid overdose admissions. In this paper, opioid-related healthcare data from Froedtert Health Medical System in Wisconsin are analyzed and machine learning models are proposed to predict the LOS of opioid overdose admitted patients. We also determine important features that impact the LOS. To explore the factors that significantly influence the LOS, we implemented recursive feature elimination (RFE) to select important features from the data. Since the data set is imbalanced, we applied two imbalanced learning approaches to tackle that, namely, SMOTE and imbalanced learning models. Several machine learning models were constructed and validated with 10 iterations of 10-fold cross validation, and we fine-tuned the models with the highest f1 score and AUC score. Random Forest Classifier outperforms other models trained on the oversampled data set (AUC = 0.81, precision = 0.72, recall = 0.69). Easy Ensemble Classifier, which is trained on the imbalanced data set, has a better performance in predicting longer stays (AUC = 0.80, precision = 0.70, recall = 0.74). Finally, important features identified from the predictive models can be used to determine at-risk patients of longer LOS and provide appropriate preventive care and resources.