Understanding Reasons for Medication Nonadherence: An Exploration in Social Media Using Sentiment-Enriched Deep Learning Approach

Jiaheng Xie, Daniel Dajun Zeng, Xiao Liu, Xiao Fang

Research output: Contribution to conferencePaperpeer-review

Abstract

Medication nonadherence (MNA) refers to the behavior when patients do not fill prescriptions. To take proactive measures and prevent harmful outcomes, the stakeholders need to understand patients’ reasons for MNA. Current studies attempt to provide one-size-fits-all solutions to the “average patients� and utilize survey or experiment design with small sample sizes to obtain a snapshot of this issue. To address these issues, we develop a semantically enhanced deep learning approach to detecting patient and drug-specific reasons for MNA using health social media data. Our model reached a precision of 86.43%, a recall of 92.53%, and an F1- score of 89.38%. This study contributes to information systems research by designing a deep-learning-based framework for detecting tailored reasons for MNA in real time. The framework is generalizable to understand motivations of various human behaviors. We also contribute to healthcare IT by discovering previously unknown MNA reasons from online health IT platforms.

Original languageEnglish (US)
StatePublished - 2018
Externally publishedYes
Event38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017 - Seoul, Korea, Republic of
Duration: Dec 10 2017Dec 13 2017

Other

Other38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017
CountryKorea, Republic of
CitySeoul
Period12/10/1712/13/17

Keywords

  • Deep learning
  • Healthcare information systems
  • Medication nonadherence
  • Precision medicine

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

Fingerprint Dive into the research topics of 'Understanding Reasons for Medication Nonadherence: An Exploration in Social Media Using Sentiment-Enriched Deep Learning Approach'. Together they form a unique fingerprint.

Cite this