Predicting Opioid Use Disorder from Longitudinal Healthcare Data using Multi-stream Transformer
This addresses the public health crisis of Opioid Use Disorder by improving prediction accuracy for healthcare applications, though it appears incremental as it builds on transformer methods.
The paper tackled predicting Opioid Use Disorder from longitudinal healthcare data by proposing a multi-stream transformer model called MUPOD, which showed significantly better performance than traditional and recent deep learning models on data from 392,492 patients with long-term back pain.
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.