Use of computational tools as support to the cross-mapping method between clinical terminologies

Abstract

Objective: to reflect on the use of computational tools in the cross-mapping method between clinical terminologies. Method: reflection study. Results: the cross-mapping method consists of obtaining a list of terms through extraction and normalization; the connection between the terms of the list and those of the reference base, by means of predefined rules; and grouping of the terms into categories: exact or partial combination or, in more detail, similar term, more comprehensive term, more restricted term and non-agreeing term. Performed manually in many studies, it can be automated with the use of the Unified Medical Language System (UMLS). Obtaining the terms list can occur automatically by natural language processing algorithms, being that the use of rules to identify information in texts allows the expert’s knowledge to be coupled to the algorithm, and it can be performed by techniques based on Machine Learning. When it comes to mapping terms using the 7-Axis model of the International Classification for Nursing Practice (ICNP®), the process can also be automated through natural language processing algorithms such as POS-tagger and the syntactic parser. Conclusion: the cross-mapping method can be intensified by the use of natural language processing algorithms. However, even in cases of automatic mapping, the validation of the results by specialists should not be discarded.