论文标题
当深度学习符合因果推理时：从现实世界数据中重新利用药物的计算框架
When deep learning meets causal inference: a computational framework for drug repurposing from real-world data
论文作者
论文摘要
药物重新定位是确定现有药物的新用途的有效策略，从而提供了从长凳到床边的最快过渡。现有的药物重新利用方法，主要关注临床前信息时可能存在转化问题。现实世界数据（RWD），例如电子健康记录和保险索赔，为许多药物提供了大量用户的信息。在这里，我们提出了一个有效且易于定制的框架，用于使用RWD的回顾性分析来生成和测试多个候选药物以重新利用药物。我们的框架以良好的因果推论和深度学习方法为基础，模拟了大规模医学索赔数据库中存在的药物的随机临床试验。我们通过评估55个重新利用药物对各种疾病预后的冠状动脉疾病（CAD）案例研究（CAD）的框架。我们获得了6种候选药物，这些药物可显着改善CAD结果，但尚未用于治疗CAD，为药物重新利用铺平了道路。
Drug repurposing is an effective strategy to identify new uses for existing drugs, providing the quickest possible transition from bench to bedside. Existing methods for drug repurposing that mainly focus on pre-clinical information may exist translational issues when applied to human beings. Real world data (RWD), such as electronic health records and insurance claims, provide information on large cohorts of users for many drugs. Here we present an efficient and easily-customized framework for generating and testing multiple candidates for drug repurposing using a retrospective analysis of RWDs. Building upon well-established causal inference and deep learning methods, our framework emulates randomized clinical trials for drugs present in a large-scale medical claims database. We demonstrate our framework in a case study of coronary artery disease (CAD) by evaluating the effect of 55 repurposing drug candidates on various disease outcomes. We achieve 6 drug candidates that significantly improve the CAD outcomes but not have been indicated for treating CAD, paving the way for drug repurposing.
