Analytic Scheme to Organize Large Archived Data into Hierarchical Affinity: A Case of Adverse Outcome Reports of the U.S. Food and Drug Administration Database

机译：分析方案将大型存档数据组织成分层亲和力：美国食品和药物管理局数据库的不利结果报告的情况

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While current information technologies allow to collect large data sets, using the archived data is often complex, time consuming, or simply impossible. For example, less than millions of reports on medical device malfunction, patient injury and death are collected every year in the Manufacturer and User-Facility Device Experience (MAUDE) database. An adverse event is manually classified according to a hierarchical coding system. The current coding system uses an arbitrary taxonomy, which causes redundant, too general, or too specific categories. Fixed categories fail to house the unprecedented observations, resulting in loss of information. Departmentalized categories tend to prevent holistic understanding of a problem. In an effort to meaningfully organize archived data, this study suggests an analytic scheme to organize categories hierarchically. Principles and relevant measures based on the theories of category representation are proposed. Also, text mining algorithm implements flexible restructuring of categories with minimal human intervention, facilitating automatic updates to maintain adaptability to unprecedented observations. To demonstrate the benefit of the proposed categorization scheme, a case study is conducted is to generate taxonomy based on a set of retraction problems in MAUDE. Results have implications for the benefits of using flexible taxonomy to categorize archived data.

机译：虽然当前信息技术允许收集大数据集，但使用归档数据通常复杂，耗时，或根本不可能。例如，每年在制造商和用户设施设备体验（Maude）数据库中每年收集少于数百万的医疗器械故障，患者伤害和死亡。根据分层编码系统手动分类不利事件。当前的编码系统使用任意分类，导致冗余，过于一般或太特定的类别。固定类别未能容纳前所未有的观察，导致信息丢失。部门的类别倾向于防止整体理解问题。努力有意义地组织存档数据，本研究表明分析方案分层组织类别。提出了基于类别代表理论的原则和相关措施。此外，文本挖掘算法可以灵活地重组类别，具有最小的人为干预，促进自动更新，以保持对前所未有的观察的适应性。为了证明拟议的分类方案的益处，进行了案例研究是基于一套摩尔德的撤回问题来生成分类。结果对利用灵活分类物分类归档数据的益处具有影响。

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《Annual Conference and Expo of the Institute of Industrial Engineers》|2014年||共10页
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作者
Inki Kim; Gul E. Okudan Kremer;
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中图分类 T-53;
关键词
Archived data; Hierarchical Categorization; Medical Device Error; Text Mining; Category Representation;

机译：存档数据;分层分类;医疗器械错误;文本挖掘;类别表示;

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4. Analytic Scheme to Organize Large Archived Data into Hierarchical Affinity: A Case of Adverse Outcome Reports of the U.S. Food and Drug Administration Database [C] . Inki Kim, Gul E. Okudan Kremer Annual Conference and Expo of the Institute of Industrial Engineers . 2014

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5. Communicating Data Visually: Data Presentation Formats at U.S. Food and Drug Administration Drug Advisory Committee Meetings. [D] . Smith, Lesley M. 2011

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Analytic Scheme to Organize Large Archived Data into Hierarchical Affinity: A Case of Adverse Outcome Reports of the U.S. Food and Drug Administration Database

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