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使用MapReduce挖掘SNOMED CT演变中的关系反转

Mining Relation Reversals in the Evolution of SNOMED CT Using MapReduce.

作者信息

Tao Shiqiang, Cui Licong, Zhu Wei, Sun Mengmeng, Bodenreider Olivier, Zhang Guo-Qiang

机构信息

Department of EECS, Case Western Reserve University, Cleveland, OH, USA ; Division of Medical Informatics, Case Western Reserve University, Cleveland, OH, USA.

Department of EECS, Case Western Reserve University, Cleveland, OH, USA.

出版信息

AMIA Jt Summits Transl Sci Proc. 2015 Mar 23;2015:46-50. eCollection 2015.

Abstract

Relation reversals in ontological systems refer to such patterns as a path from concept A to concept B in one version becoming a path with the position of A and B switched in another version. We present a scalable approach, using cloud computing, to systematically extract all hierarchical relation reversals among 8 SNOMED CT versions from 2009 to 2014. Taking advantage of our MapReduce algorithms for computing transitive closure and large-scale set operations, 48 reversals were found through 28 pairwise comparison of the 8 versions in 18 minutes using a 30-node local cloud, to completely cover all possible scenarios. Except for one, all such reversals occurred in three sub-hierarchies: Body Structure, Clinical Finding, and Procedure. Two (2) reversal pairs involved an uncoupling of the pair before the is-a coupling is reversed. Twelve (12) reversal pairs involved paths of length-two, and none (0) involved paths beyond length-two. Such reversals not only represent areas of potential need for additional modeling work, but also are important for identifying and handling cycles for comparative visualization of ontological evolution.

摘要

本体系统中的关系反转是指这样一种模式

在一个版本中从概念A到概念B的路径,在另一个版本中变成了A和B位置互换的路径。我们提出了一种利用云计算的可扩展方法,用于系统地提取2009年至2014年8个SNOMED CT版本之间的所有层次关系反转。利用我们用于计算传递闭包和大规模集合运算的MapReduce算法,通过使用30节点的本地云在18分钟内对8个版本进行28次成对比较,发现了48次反转,从而完全覆盖了所有可能的情况。除了一次反转外,所有这些反转都发生在三个子层次结构中:身体结构、临床发现和手术操作。有两(2)对反转涉及在“是一个”耦合反转之前该对的解耦。十二(12)对反转涉及长度为二的路径,没有(0)对涉及长度超过二的路径。这种反转不仅代表了可能需要额外建模工作的领域,而且对于识别和处理本体进化的比较可视化中的循环也很重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b4e/4525241/f69c1c103bb6/2091696f2.jpg

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