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The Drug Data to Knowledge Pipeline: Large-Scale Claims Data Classification for Pharmacologic Insight

机译:药品数据到知识管道:大规模理赔数据分类以获取药理学见解

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摘要

In biomedical informatics, assigning drug codes to categories is a common step in the analysis pipeline. Unfortunately, incomplete mappings are the norm rather than the exception with coverage values less than 85% not uncommon. Here, we perform this linking task on a nationwide insurance claims database with over 13 million members who were dispensed, according to National Drug Codes (NDCs), over 50,000 unique product forms of medication. The chosen approach employs Cerner Multum’s VantageRx and the U.S. National Library of Medicine’s RxMix. As a result, 94.0% of the NDCs were successfully mapped to categories used by common drug terminologies, e.g., Anatomical Therapeutic Chemical (ATC). Implemented as an SQL database and scripts, the approach is generic and can be setup for a new data set in a few hours. Thus, the method is a viable option for large-scale drug classification.
机译:在生物医学信息学中,将药物代码分配给类别是分析流程中的常见步骤。不幸的是,不完整的映射是常态,而不是例外,覆盖率值小于85%并不少见。在这里,我们在全国保险理赔数据库上执行此链接任务,根据国家药品法规(NDC),有超过1300万会员被分配了超过50,000种独特的药品。选择的方法采用了Cerner Multum的VantageRx和美国国家医学图书馆的RxMix。结果,成功将94.0%的NDC映射到常用药物术语使用的类别,例如,解剖治疗化学(ATC)。该方法实现为SQL数据库和脚本,具有通用性,可以在几个小时内针对新数据集进行设置。因此,该方法是大规模药物分类的可行选择。

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