Discovering barriers to opioid addiction treatment from social media: A similarity network-based deep learning approach

Jiaheng Xie, Zhu Zhang, Xiao Liu, Daniel Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication40th International Conference on Information Systems, ICIS 2019
PublisherAssociation for Information Systems
ISBN (Electronic)9780996683197
StatePublished - 2019
Event40th International Conference on Information Systems, ICIS 2019 - Munich, Germany
Duration: Dec 15 2019Dec 18 2019

Publication series

Name40th International Conference on Information Systems, ICIS 2019

Conference

Conference40th International Conference on Information Systems, ICIS 2019
Country/TerritoryGermany
CityMunich
Period12/15/1912/18/19

Keywords

  • Data science
  • Deep learning
  • Opioid addiction
  • Text mining

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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