Detecting modeling inconsistencies in SNOMED CT using a machine learning technique
Detecting modeling inconsistencies in SNOMED CT using a machine learning technique
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DOI：
10.1016/j.ymeth.2020.05.019
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发表时间：
2020-07-01
期刊：
影响因子：
4.8
通讯作者：
Qazi, Kashifuddin
中科院分区：
文献类型：
--
作者：
Agrawal, Ankur;Qazi, Kashifuddin
SNOMED CT是一个全面且不断发展的临床参考术语，已被广泛采用为通用词汇，以促进电子健康记录之间的互操作性。由于其在医疗保健中的重要性，质量保证成为SNOMED CT生命周期中不可或缺的一部分。虽然手动审计SNOMED CT中的每个概念都很困难，而且劳动强度很大，但在没有任何上下文的情况下识别概念建模中的不一致可能是具有挑战性的。需要算法技术来识别SNOMED CT中的建模不一致(如果有的话)。这项研究提出了一种基于上下文的机器学习质量保证技术来识别SNOMED CT中可能需要审计的概念。临床发现和程序层次被用作检验该方法的有效性的试验床。审计结果表明，该方法在算法认为不一致的概念对中发现了72%的不一致。该方法被证明是有效的，既能最大限度地提高校正的效率，又能提供一个识别不一致的环境。这些方法，加上SNOMED International自己的努力，可以极大地帮助减少SNOMED CT的不一致。
SNOMED CT is a comprehensive and evolving clinical reference terminology that has been widely adopted as a common vocabulary to promote interoperability between Electronic Health Records. Owing to its importance in healthcare, quality assurance becomes an integral part of the lifecycle of SNOMED CT. While, manual auditing of every concept in SNOMED CT is difficult and labor intensive, identifying inconsistencies in the modeling of concepts without any context can be challenging. Algorithmic techniques are needed to identify modeling inconsistencies, if any, in SNOMED CT. This study proposes a context-based, machine learning quality assurance technique to identify concepts in SNOMED CT that may be in need of auditing. The Clinical Finding and the Procedure hierarchies are used as a testbed to check the efficacy of the method. Results of auditing show that the method identified inconsistencies in 72% of the concept pairs that were deemed inconsistent by the algorithm. The method is shown to be effective in both maximizing the yield of correction, as well as providing a context to identify the inconsistencies. Such methods, along with SNOMED International's own efforts, can greatly help reduce inconsistencies in SNOMED CT.