Opioid Use Disorder (OUD) is a public health crisis costing the US billions of dollars annually in healthcare, lost workplace productivity, and crime. Analyzing longitudinal healthcare data is critical in addressing many real-world problems in healthcare. Leveraging the real-world longitudinal healthcare data, we propose a novel multi-stream transformer model called MUPOD for OUD identification. MUPOD is designed to simultaneously analyze multiple types of healthcare data streams, such as medications and diagnoses, by attending to segments within and across these data streams. Our model tested on the data from 392,492 patients with long-term back pain problems showed significantly better performance than the traditional models and recently developed deep learning models.

Original languageEnglish
Pages (from-to)476-485
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
StatePublished - 2021

Bibliographical note

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©2021 AMIA - All rights reserved.

ASJC Scopus subject areas

  • Medicine (all)


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