Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
Department of Computer Information Systems, Borough of Manhattan Community College, City University of New York, New York, NY, 10007, USA.
BMC Med Inform Decis Mak. 2020 Dec 15;20(Suppl 10):272. doi: 10.1186/s12911-020-01290-z.
While enrichment of terminologies can be achieved in different ways, filling gaps in the IS-A hierarchy backbone of a terminology appears especially promising. To avoid difficult manual inspection, we started a research program in 2014, investigating terminology densities, where the comparison of terminologies leads to the algorithmic discovery of potentially missing concepts in a target terminology. While candidate concepts have to be approved for import by an expert, the human effort is greatly reduced by algorithmic generation of candidates. In previous studies, a single source terminology was used with one target terminology.
In this paper, we are extending the algorithmic detection of "candidate concepts for import" from one source terminology to two source terminologies used in tandem. We show that the combination of two source terminologies relative to one target terminology leads to the discovery of candidate concepts for import that could not be found with the same "reliability" when comparing one source terminology alone to the target terminology. We investigate which triples of UMLS terminologies can be gainfully used for the described purpose and how many candidate concepts can be found for each individual triple of terminologies.
The analysis revealed a specific configuration of concepts, overlapping two source and one target terminology, for which we coined the name "fire ladder" pattern. The three terminologies in this pattern are tied together by a kind of "transitivity." We provide a quantitative analysis of the discovered fire ladder patterns and we report on the inter-rater agreement concerning the decision of importing candidate concepts from source terminologies into the target terminology. We algorithmically identified 55 instances of the fire ladder pattern and two domain experts agreed on import for 39 instances. In total, 48 concepts were approved by at least one expert. In addition, 105 import candidate concepts from a single source terminology into the target terminology were also detected, as a "beneficial side-effect" of this method, increasing the cardinality of the result.
We showed that pairs of biomedical source terminologies can be transitively chained to suggest possible imports of concepts into a target terminology.
虽然可以通过不同的方式丰富术语，但填补术语 IS-A 层次结构主干中的空白似乎特别有希望。为了避免困难的手动检查，我们于 2014 年开始了一个研究项目，研究术语密度，其中术语的比较导致算法发现目标术语中可能缺失的概念。虽然候选概念必须由专家批准导入，但通过算法生成候选概念可以大大减少人工工作量。在之前的研究中，使用一个源术语和一个目标术语。
在本文中，我们将从一个源术语到两个串联使用的源术语扩展算法检测“导入候选概念”。我们表明，与将一个源术语与目标术语进行比较相比，将两个源术语与一个目标术语进行组合会发现候选概念导入，这些候选概念在使用相同的“可靠性”进行比较时无法找到。我们研究了哪些 UMLS 术语可以用于描述的目的，以及对于每个单独的术语对可以找到多少候选概念。
分析揭示了一种特定的概念配置，重叠了两个源术语和一个目标术语，我们为其命名为“消防梯”模式。该模式中的三个术语通过一种“传递性”联系在一起。我们对发现的消防梯模式进行了定量分析，并报告了关于从源术语到目标术语导入候选概念的决策的评分者间一致性。我们算法地识别了 55 个消防梯模式实例，两名领域专家一致同意将 39 个实例导入。总共至少有一位专家批准了 48 个概念。此外，还从单个源术语检测到 105 个导入目标术语的候选概念，这是该方法的“有益副作用”，增加了结果的基数。
我们表明，一对生物医学源术语可以通过传递性链接来暗示向目标术语中导入概念的可能性。
