Understanding Opioid Addiction with Similarity Network-Based Deep Learning

Abstract

Opioid use disorder (OUD) refers to the physical and psychological reliance on opioids. OUD costs the US healthcare systems $504 billion annually and poses significant mortality risk for patients. Understanding and mitigating the barriers to OUD treatment is a high-priority area for healthcare and IS researchers, practitioners, and policymakers. Current OUD treatment studies largely rely on surveys and reviews. However, the response rate of these surveys is low because patients are reluctant to share their OUD experience for fear of stigma in society. In this paper, we explore social media as a new source of data to study OUD treatments. Drug users increasingly participate in social media to share their experience anonymously. Yet their voice in social media has not been utilized in past studies. We develop the SImilarity Network-based DEep Learning (SINDEL) to discover barriers to OUD treatment from the patient narratives and address the challenge of morphs. SINDEL significantly outperforms state-of-the-art baseline models, reaching an F1 score of 76.79%. Thirteen types of OUD treatment barriers were identified and verified by domain experts. This study contributes to IS literature by proposing a novel deep-learning-based analytical approach with impactful implications for health practitioners.