Institute for Biomedical Informatics.
Department of Computer Science.
AMIA Annu Symp Proc. 2022 Feb 21;2021:476-485. eCollection 2021.
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.
阿片类药物使用障碍（OUD）是一场公共健康危机，每年给美国造成数十亿美元的医疗护理、工作场所生产力损失和犯罪成本。分析纵向医疗保健数据对于解决医疗保健领域的许多实际问题至关重要。我们利用真实世界的纵向医疗保健数据，提出了一种名为 MUPOD 的新型多流转换器模型，用于 OUD 识别。MUPOD 旨在通过关注这些数据流内部和跨数据流的各个部分，同时分析多种类型的医疗保健数据流，如药物和诊断。我们在 392492 名长期背痛患者的数据上进行了测试，结果表明，该模型的性能明显优于传统模型和最近开发的深度学习模型。
