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

Abstract

People are prone to developing opioid dependence and other health problems due to regular non-medical use, prolonged use, misuse, and use without medical supervision. 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 length of stay (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 implement machine learning algorithms, namely, Random Forest and XGBoost, to select important features from the data. Predictive models such as Random Forest regressor, Gradient Boost regressor, Support Vector Machine and k-Neighbors regressor are conducted and trained on the top-50, 100, 300, 650, and 1000 important features. We propose to evaluate the regression models using Mean Squared Error (MSE) and R-squared.