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使用关联规则挖掘减少医疗服务提供者下达的自由文本沟通医嘱。

Reducing free-text communication orders placed by providers using association rule mining.

作者信息

Hajihashemi Zahra, Pancoast Paul

机构信息

University of Missouri, Computer Science Department, Columbia, MO, USA.

出版信息

AMIA Annu Symp Proc. 2012;2012:1254-9. Epub 2012 Nov 3.

Abstract

Electronic health record (EHR) systems are used to collect, store and retrieve the details of patient care. Computer Provider Order Entry (CPOE) is a process by which providers directly enter patient care orders into the EHR. Providers may enter free-text orders when they are unable to find standard orders. These free-text orders require translation into a structured order which reducing efficiency, may bypass duplicate checking and can be associated with medical errors. To overcome these problems we developed a system to automatically detect free-text orders and assign them to the appropriate order categories. This system applies association rule mining on structured orders to extract the patterns of orders in the related categories. The extracted patterns were tested on a set of free-text orders for evaluation and to determine the closest matching category of structured orders. This process may be used to improve future iterations of CPOE applications.

摘要

电子健康记录(EHR)系统用于收集、存储和检索患者护理的详细信息。计算机医生医嘱录入(CPOE)是指医生直接将患者护理医嘱录入电子健康记录的过程。当医生无法找到标准医嘱时,他们可能会录入自由文本医嘱。这些自由文本医嘱需要转换为结构化医嘱,这可能会降低效率、绕过重复检查并且可能与医疗差错相关。为克服这些问题,我们开发了一个系统,用于自动检测自由文本医嘱并将其分配到适当的医嘱类别。该系统对结构化医嘱应用关联规则挖掘,以提取相关类别中医嘱的模式。提取的模式在一组自由文本医嘱上进行测试,以进行评估并确定最匹配的结构化医嘱类别。这一过程可用于改进CPOE应用程序的未来迭代。

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