Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1028-1031. doi: 10.1109/EMBC48229.2022.9872018.
Drug overdose has become a public health crisis in the United States with devastating consequences. However, most of the drug overdose incidences are the consequence of recitative polysubstance usage over a defined period of time which can be happened by either the intentional usage of required drug with other drugs or by accident. Thus, predicting the effects of polysubstance usage is extremely important for clinicians to decide which combination of drugs should be prescribed. Recent advancement of structural causal models can provide ample insights of causal effects from observational data via identifiable causal directed graphs. In this paper, we propose a system to estimate heterogeneous concurrent drug usage effects on overdose estimation, that consists of efficient co-variate selection, sub-group selection and heterogeneous causal effect estimation. We apply our framework to answer a critical question, 'can concurrent usage of benzodiazepines and opioids have heterogeneous causal effects on the opioid overdose epidemic?' Using Truven MarketScan claim data collected from 2001 to 2013 have shown significant promise of our proposed framework's efficacy. Latest paper and codes can be found here https://arxiv.org/abs/2105.07224.
药物过量在美国已成为公共卫生危机，造成了灾难性的后果。然而，大多数药物过量事件都是在一段时间内反复使用多种物质的结果，这种情况既可能是故意将所需药物与其他药物一起使用，也可能是偶然发生的。因此，预测多种物质使用的效果对于临床医生决定应开哪种药物组合非常重要。最近结构因果模型的进步可以通过可识别的因果有向图从观察数据中提供大量因果效应的见解。在本文中，我们提出了一个系统来估计对过量估计的异质并发药物使用效果，该系统包括有效的协变量选择、子组选择和异质因果效应估计。我们应用我们的框架来回答一个关键问题，“苯二氮䓬类药物和阿片类药物的同时使用是否会对阿片类药物过量流行产生异质的因果影响？” 使用从 2001 年到 2013 年收集的 Truven MarketScan 索赔数据表明，我们提出的框架的有效性具有很大的潜力。最新的论文和代码可以在这里找到：https://arxiv.org/abs/2105.07224。
