Business Strategy & Risk Management, Republic Bank & Trust Company, 601 W. Market Street, Louisville, KY 40202, USA.
J Biomed Inform. 2012 Apr;45(2):224-30. doi: 10.1016/j.jbi.2011.10.009. Epub 2011 Nov 10.
Many problems arise when linking medical records from multiple databases. Matching these data to other data is problematic since even small errors, such as data entry errors, different text format, and missing data, can prevent the exact-match algorithms. Evidence from previous studies suggested that approximate field matching represent a solution to resolve the problem by identifying equivalent string values in different representations. The purpose of this article is to explore the effectiveness of a medical record matching method using a fuzzy logic framework. This article considers quantitative measures of the typical elements in medical records, and fuzzy logic is applied to link to the linguistic concepts. Moreover, this article discusses the medical record matching from the developed framework, which is tested on a public data set. The results from the test on a public data set indicate that the medical record matching method using fuzzy logic framework provides an effective solution for dealing with linkage problems, and illustrate that the multiple valued logic method outlined can potentially be applied to address similar problems in other databases.
当链接来自多个数据库的医疗记录时，会出现许多问题。将这些数据与其他数据进行匹配存在问题，因为即使是很小的错误，如数据输入错误、不同的文本格式和数据缺失，也可能阻止精确匹配算法。来自先前研究的证据表明，近似字段匹配代表一种解决方案，可以通过识别不同表示形式中的等效字符串值来解决问题。本文的目的是探讨使用模糊逻辑框架的医疗记录匹配方法的有效性。本文考虑了医疗记录中典型元素的定量度量，并将模糊逻辑应用于链接到语言概念。此外，本文还从开发的框架讨论了医疗记录匹配，该框架在公共数据集上进行了测试。公共数据集上的测试结果表明，使用模糊逻辑框架的医疗记录匹配方法为处理链接问题提供了有效的解决方案，并说明概述的多值逻辑方法可能潜在地应用于解决其他数据库中的类似问题。
