  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 prediction. MUPOD is designed to simultaneously analyze multiple types
of healthcare data streams, such as medications and diagnoses, by finding the
attentions 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.
