Using Electronic Medical Records and Clinical Notes to Predict the Outcome of Opioid Treatment Program.

Abstract

Opioid addiction is a serious public health problem in the US, and this study aimed to explore how natural language processing (NLP) can be used to identify factors that contribute to distress in individuals with opioid addiction, and then use this information along with structured data to predict the outcome of opioid treatment programs (OTP). The study analyzed medical records data and clinical notes of 1,364 patients, out of which 136 succeeded in the program and 1,228 failed. The results showed that several factors influenced the success of patients in the program, including sex, race, education, employment, secondary substance, tobacco use, and type of residences. XGBoost with down sampling was the best model. The accuracy of the model was 0.71 and the AUC score was 0.64. The study highlights the importance of using both structured and unstructured data to evaluate the effectiveness of OTP.