VIEW：支持可再现数据科学的组织级交互记录链接框架
VIEW: a framework for organization level interactive record linkage to support reproducible data science
摘要：
目标：设计和评估一个通用的框架，用于交互式记录链接，使用方便的算法结合易于处理的人类智能任务（HIT;即需要人类判断的微任务），可以支持可再现的数据科学。材料与方法：真实数据的准确链接既需要自动处理定义明确的任务，也需要人工处理需要人工判断的任务（即，HITS）上的数据。我们提出了一个可重复的，交互式的，迭代的框架记录连接称为视图（可视化交互式分辨率分辨率）。我们通过整合两个常用的医院数据库，美国医院协会（AHA）的年度医院调查和CMS的医疗保险成本报告医院实施和评估VIEW。结果如下：使用VIEW迭代标准化和清理数据，我们通过使用HIT确认78个近似链接并使用HIT手动链接28家医院，以100%的精度链接了两个数据库中常见的所有德克萨斯州医院。讨论内容：医院名称和地址的相似性以及医院属性随时间的动态性，使得不可能为医院建立一个可以随时间维持的全自动链接系统。VIEW是一款支持可再现半自动化流程的软件，该流程可以生成和跟踪HIT，以便手动查看和链接杂乱的数据元素，例如已合并的医院。结论：有效的软件可以支持记录链接的交互式和迭代过程，而精心设计的HIT可以简化链接过程，以支持使用混乱的真实数据的高质量可复制研究。
英文摘要：
Objective: To design and evaluate a general framework for interactive record linkage using a convenient algorithm combined with tractable Human Intelligent Tasks (HITs; i.e. micro tasks requiring human judgment) that can support reproducible data science. Materials and Methods: Accurate linkage of real data requires both automatic processing of well-defined tasks and human processing of tasks that require human judgment (i.e., HITs) on messy data. We present a reproducible, interactive, and iterative framework for record linkage called VIEW (Visual Interactive Entity-resolution Workbench). We implemented and evaluated VIEW by integrating two commonly used hospital databases, the American Hospital Association (AHA) Annual Survey of Hospitals and the Medicare Cost Reports for Hospitals from CMS. Results: Using VIEW to iteratively standardize and clean the data, we linked all Texas hospitals common in both databases with 100% precision by confirming 78 approximate linkages using HITs and manually linking 28 hospitals using HITs. Discussion: Similarities in hospital names and addresses and the dynamic nature of hospital attributes over time make it impossible to build a fully automated linkage system for hospitals that can be maintained over time. VIEW is a software that supports a reproducible semi-automated process that can generate and track HITs to be reviewed and linked manually for messy data elements such as hospitals that have been merged. Conclusion: Effective software that can support the interactive and iterative process of record linkage, and well-designed HITs can streamline the linkage processes to support high quality replicable research using messy real data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
