Explainable Deep Learning Applied to Understanding Opioid Use Disorder and Its Risk Factors

T. Elizabeth Workman, Qing Zeng-Treitler, Yijun Shao, Joel Kupersmith, Friedhelm Sandbrink, Joseph L. Goulet, Nawar M. Shaar, Christopher Spevak, Cynthia Brandt, Marc R. Blackman

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

Abstract

Opioid Use Disorder is an international crisis, affecting many populations. Deep learning models can potentially predict opioid use disorder, but provide little insight to how predictions are derived. Impact scores, a new development in explainable artificial intelligence, measure how individual features affect deep learning outcomes. We modeled clinical visits to predict opioid use disorder, computed impact scores, and compared them to odds log ratios from logistic regression. Impact scores were generally comparable to odds log ratios, in providing insight to opioid abuse risk, but from a better-performing method than logistic regression.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4883-4888
Number of pages6
ISBN (Electronic)9781728108582
DOIs
StatePublished - Dec 2019
Externally publishedYes
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: Dec 9 2019Dec 12 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
CountryUnited States
CityLos Angeles
Period12/9/1912/12/19

Keywords

  • Deep Learning
  • Explainable AI
  • Impact Scores
  • Opioid Use Disorder

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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