Identifying risk of opioid use disorder for patients taking opioid medications with deep learning.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jul 30, 2021

Abstract

OBJECTIVE: The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions.

Authors

  • Xinyu Dong
    Stony Brook University, Stony Brook, NY.
  • Jianyuan Deng
    Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States.
  • Sina Rashidian
    Stony Brook University, Stony Brook, NY.
  • Kayley Abell-Hart
    Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA.
  • Wei Hou
    Institute of Special Animal and Plant Sciences, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
  • Richard N Rosenthal
    Stony Brook University, Stony Brook, NY.
  • Mary Saltz
    Stony Brook University, Stony Brook, NY.
  • Joel H Saltz
  • Fusheng Wang
    Stony Brook University, Stony Brook, NY.

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