Predicting post-surgical opioid consumption using perioperative surgical data

Abstract

Improper consumption of prescription opioids is a massive public health issue in the United States currently. Here, we propose one approach of tackling this issue through using machine learning techniques to predict opioid consumption post discharge for surgical patients. Through the data collected from surgical patients at BIDMC, relevant features will be identified and used to predict if patients high, outlier consumption. Using logistic regression and gradient boosted decision trees, model performance were evaluated at AUCs of 0.7270 and 0.7289 respectively.