@inproceedings{31ccc432c3b44b678ecf4b0211de2d51,
title = "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.",
keywords = "Computational data science, Deep learning, Health analytics, Text mining",
author = "Jiaheng Xie and Zhu Zhang and Xiao Liu and Daniel Zeng",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 7th International Conference for Smart Health, ICSH 2019 ; Conference date: 01-07-2019 Through 02-07-2019",
year = "2019",
doi = "10.1007/978-3-030-34482-5_12",
language = "English (US)",
isbn = "9783030344818",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "134--141",
editor = "Hsinchun Chen and Daniel Zeng and Xiangbin Yan and Chunxiao Xing",
booktitle = "Smart Health - International Conference, ICSH 2019, Proceedings",
}