Torres Fernanda Broering Gomes, Gomes Denilsen Carvalho, Hino Adriano Akira Ferreira, Moro Claudia, Cubas Marcia Regina
Graduate Program in Health Technology Pontificia Universidade Católica do Paraná Curitiba Brazil.
JMIR Nurs. 2020 Jun 9;3(1):e18501. doi: 10.2196/18501. eCollection 2020 Jan-Dec.
Cross-mapping establishes equivalence between terms from different terminology systems, which is useful for interoperability, updated terminological versions, and reuse of terms. Due to the number of terms to be mapped, this work can be extensive, tedious, and thorough, and it is susceptible to errors; this can be minimized by automated processes, which use computational tools.
The aim of this study was to compare the results of manual and automated term mapping processes.
In this descriptive, quantitative study, we used the results of two mapping processes as an empirical basis: manual, which used 2638 terms of nurses' records from a university hospital in southern Brazil and the International Classification for Nursing Practice (ICNP); and automated, which used the same university hospital terms and the primitive terms of the ICNP through MappICNP, an algorithm based on rules of natural language processing. The two processes were compared via equality and exclusivity assessments of new terms of the automated process and of candidate terms.
The automated process mapped 569/2638 (21.56%) of the source bank's terms as identical, and the manual process mapped 650/2638 (24.63%) as identical. Regarding new terms, the automated process mapped 1031/2638 (39.08%) of the source bank's terms as new, while the manual process mapped 1251 (47.42%). In particular, manual mapping identified 101/2638 (3.82%) terms as identical and 429 (16.26%) as new, whereas the automated process identified 20 (0.75%) terms as identical and 209 (7.92%) as new. Of the 209 terms mapped as new by the automated process, it was possible to establish an equivalence with ICNP terms in 48 (23.0%) cases. An analysis of the candidate terms offered by the automated process to the 429 new terms mapped exclusively by the manual process resulted in 100 (23.3%) candidates that had a semantic relationship with the source term.
The automated and manual processes map identical and new terms in similar ways and can be considered complementary. Direct identification of identical terms and the offering of candidate terms through the automated process facilitate and enhance the results of the mapping; confirmation of the precision of the automated mapping requires further analysis by researchers.
交叉映射可在不同术语系统的术语之间建立等价关系，这对于互操作性、术语版本更新及术语复用很有用。由于需要映射的术语数量众多，这项工作可能会很广泛、繁琐且需要彻底完成，并且容易出错；通过使用计算工具的自动化流程可将这种情况降至最低。
本研究旨在比较手动和自动术语映射流程的结果。
在这项描述性定量研究中，我们将两个映射流程的结果作为实证依据：手动流程使用了巴西南部一家大学医院护士记录中的2638个术语以及国际护理实践分类（ICNP）；自动流程则通过MappICNP（一种基于自然语言处理规则的算法）使用了同一家大学医院的术语和ICNP的原始术语。通过对自动流程的新术语和候选术语与手动流程进行等同性和排他性评估来比较这两个流程。
自动流程将源库中569/2638（21.56%）的术语映射为相同，手动流程将650/2638（24.63%）的术语映射为相同。关于新术语，自动流程将源库中1031/2638（39.08%）的术语映射为新术语，而手动流程映射了1251个（47.42%）。具体而言，手动映射将101/2638（3.82%）的术语识别为相同，429个（16.26%）为新术语，而自动流程识别出20个（0.75%）术语为相同，209个（7.92%）为新术语。在自动流程映射为新术语的209个术语中，有48个（2
