Predicting Opioid Overdose Readmission and Opioid Use Disorder with Machine Learning

Opioid use disorder (OUD) is a medical condition associated with problematic patterns of opioid use that cause interpersonal and social impairment. This research demonstrates how supervised machine learning can be used to predict patients at risk of hospital readmission following opioid overdose, and to predict patients at risk of developing OUD. Two labeled datasets were built from deidentified hospital data provided by a Level I Trauma Center Hospital. Several machine learning models were constructed (logistic regression, random forest, support vector machine, AdaBoost, XGBoost) and validated with 10 iterations of 10-fold cross validation. The XGBoost classifier can sufficiently predict patients at risk for OUD (AUC = 0.78, precision = 0.71, recall = 0.53). This work can assist providers in determining appropriate preventive care and resources for at-risk patients.