Predicting Opioid Overdose Risk of Patients with Opioid Prescriptions Using Electronic Health Records Based on Temporal Deep Learning

Abstract

Background: The US is experiencing an opioid epidemic, such that opioid overdose is causing more than 100 deaths per day. Identifying patients at high risk of Opioid Overdose (OD) can help to make early clinical interventions to reduce the risk of OD. We aim to build a deep learning model that can predict the patients at high risk for opioid overdose and identify most relevant features. Method: The study included the information of 5,231,614 patients from the Health Facts database with at least one opioid prescription between January 1, 2008 and December 31, 2017. Potential predictors (n = 1185) were extracted to build a feature matrix for prediction. A Long Short-Term Memory (LSTM) based model was built to predict overdose risk in the next hospital visit. Prediction performance was compared with other machine learning methods assessed using machine learning metrics. Findings: Our temporal deep learning model built upon LSTM outperformed the other methods on opioid overdose prediction. It achieved the highest F-1 score (F-1 score: 0.8065, AUCROC: 0.8885). The model is also able to reveal top ranked predictive features, including medications and vital signs. Interpretation: This study demonstrates that a temporal deep learning based predictive model can achieve promising results on identifying risk of opioid overdose of patients using the history of electronic health records. It provides an alternative informatics-based approach to improving clinical decision support for possible early detection and intervention to reduce opioid overdose. Funding: This work was funded partially by the Stony Brook University OVPR Seed Grant 1158484-63845-6. Declaration of Interests: The authors do not have any conflicts of interest to disclose. Ethics Approval Statement: All the data used in the study are de-identified, and the study is approved by Stony Brook University.