Using Natural Language Processing and Machine Learning to Identify Hospitalized Patients with Opioid Use Disorder

Abstract

Opioid use disorder (OUD) represents a global public health crisis that challenges classic clinical decision making. As existing hospital screening methods are resource-intensive, patients with OUD are significantly under-detected. An automated and accurate approach is needed to improve OUD identification so that appropriate care can be provided to these patients in a timely fashion. In this study, we used a large-scale clinical database from Mass General Brigham (MGB; formerly Partners HealthCare) to develop an OUD patient identification algorithm, using multiple machine learning methods. Working closely with an addiction psychiatrist, we developed a set of hand-crafted rules for identifying information suggestive of OUD from free-text clinical notes. We implemented a natural language processing (NLP)-based classification algorithm within the Medical Text Extraction, Reasoning and Mapping System (MTERMS) tool suite to automatically label patients as positive or negative for OUD based on these rules. We further used the NLP output as features to build multiple machine learning and a neural classifier. Our methods yielded robust performance for classifying hospitalized patients as positive or negative for OUD, with the best performing feature set and model combination achieving an F1 score of 0.97. These results show promise for the future development of a real-time tool for quickly and accurately identifying patients with OUD in the hospital setting.