Department of Computer Science, Manhattan College, NY, USA.
Department of Computer Science, Manhattan College, NY, USA.
Methods. 2020 Jul 1;179:111-118. doi: 10.1016/j.ymeth.2020.05.019. Epub 2020 May 20.
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.
SNOMED CT 是一种全面且不断发展的临床参考术语，已被广泛采用作为通用词汇，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 生命周期的一个组成部分。然而，手动审核 SNOMED CT 中的每个概念既困难又耗费大量人力，而在没有任何上下文的情况下识别概念建模中的不一致性则具有挑战性。需要算法技术来识别 SNOMED CT 中是否存在建模不一致的情况。本研究提出了一种基于上下文的机器学习质量保证技术，以识别 SNOMED CT 中可能需要审核的概念。临床发现和程序层次结构被用作测试床，以检查该方法的效果。审核结果表明，该方法在被算法认为不一致的概念对中，有 72%识别出了不一致的情况。该方法在最大限度地提高校正效果方面以及提供识别不一致的上下文方面都非常有效。此类方法以及 SNOMED International 自身的努力，可以极大地帮助减少 SNOMED CT 中的不一致情况。
