Identifying Opioid Overdose and Opioid Use Disorder and Related Information from Clinical Narratives Using Large Language Models.

Abstract

Opioid overdose and opioid use disorder (OUD) remain a growing public health issue in the United States, affecting 6.1 million individuals in 2022, more than doubling the 2.5 million from 2021. Accurately identifying the opioid overdose and OUD related information is critical to study the outcomes and develop interventions. This study aims to identify opioid overdose and OUD mentions and their related information from clinical narratives. We compared encoder-based large language models (LLMs) and decoder-based generative LLMs in extracting nine crucial concepts related with opioid overdose and OUD including problematic opioid use. Through a cost-effective p-tuning algorithm, our decoder-based generative LLM, GatorTronGPT, achieved the best strict/lenient F1-score of 0.8637, and 0.9057, demonstrating the efficient of using generative LLMs for opioid overdose/OUD related information extraction. This study provided a tool to systematically extract opioid overdose, OUD, and their related information to facilitate opioid-related studies using clinical narratives.