ABSTRACT

Opioid use disorder (OUD) is an epidemic that costs the U.S. healthcare systems $504 billion annually and poses grave mortality risks. Existing studies investigated OUD treatment barriers via surveys as a means to mitigate this opioid crisis. However, the response rate of these surveys is low due to social stigma around opioids. We explore user-generated content in social media as a new data source to study OUD. We design a novel IT system, SImilarity Network-based DEep Learning (SINDEL), to discover OUD treatment barriers from patient narratives and address the challenge of morphs. SINDEL significantly outperforms state-of-the-art NLP models, reaching an F1 score of 76.79 percent. Thirteen types of treatment barriers were identified and verified by domain experts. This work contributes to information systems with a novel deep-learning-based approach for text analytics and generalized design principles for social media analytics methods. We also unveil the hurdles patients endure during the opioid epidemic.

摘要

阿片类药物使用障碍（OUD）是一种流行病，每年给美国医疗保健系统造成5040亿美元的损失，并带来严重的死亡风险。现有研究通过调查研究了OUD治疗的障碍，以此缓解这种阿片类药物危机。然而，由于围绕阿片类药物的社会污名化，这些调查的回复率很低。我们在社交媒体中探索用户生成的内容，作为研究OUD的新数据源。我们设计了一种新的IT系统，SI milarity ñ etwork基于DE EP大号收入（SINDEL），以从患者叙述中发现OUD的治疗障碍并应对形态挑战。SINDEL明显优于最新的NLP模型，F1分数达到76.79％。领域专家确定并验证了13种治疗障碍。这项工作通过一种新颖的基于深度学习的文本分析方法以及社交媒体分析方法的通用设计原则，为信息系统做出了贡献。我们还揭开了阿片类药物流行期间患者所能承受的障碍。