@inproceedings{f4f8b7554a214d309d9d1cdc08f24d76,
title = "Discovering barriers to opioid addiction treatment from social media: A similarity network-based deep learning approach",
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. Current OUD treatment studies rely on surveys with low response rate because of social stigma. In this paper, we explore social media as a new data source to study OUD treatments. 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 reaches 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 = "Data science, Deep learning, Opioid addiction, Text mining",
author = "Jiaheng Xie and Zhu Zhang and Xiao Liu and Daniel Zeng",
note = "Publisher Copyright: {\textcopyright} 40th International Conference on Information Systems, ICIS 2019. All rights reserved.; 40th International Conference on Information Systems, ICIS 2019 ; Conference date: 15-12-2019 Through 18-12-2019",
year = "2019",
language = "English (US)",
series = "40th International Conference on Information Systems, ICIS 2019",
publisher = "Association for Information Systems",
booktitle = "40th International Conference on Information Systems, ICIS 2019",
}