Use of Computational Tools as Support to the Cross-Mapping Method Between Clinical Terminologies

Gomes, Denilsen Carvalho; Oliveira, Lucas Emanuel Silva e; Cubas, Márcia Regina; Moro, Cláudia Maria Cabral

doi:10.1590/1980-265x-tce-2017-0187

Cited by 5 publications

(6 citation statements)

References 19 publications

(21 reference statements)

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“…In terms of form, the document indicates the need of formal computational representation, so that the mapping is computer readable and allows computational processing and machine translation (2) . In addition to facilitating the cross-terminology mapping process, computational representation reduce manual mapping errors and the time spent by researchers (18) .…”

Section: Iso/tr 12300:2016 Technical Documentmentioning

confidence: 99%

“…The complexity of the cross-terminology mapping process requires attention, ability and time. To optimize the resources involved, health informatics can support this process (18) through self-organizing mapping (2) , so named when computational tools are used. This enables matching terms through established rules (2) , providing direct identification of the possible candidate term or terms to be evaluated and validated by the researcher (2,26) .…”

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confidence: 99%

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ISO/TR 12300:2016 for clinical cross-terminology mapping: contribution to nursing

Torres

Gomes

Ronnau

et al. 2020

Rev. esc. enferm. USP

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This theoretical and reflective study aimed to assess the contribution of the ISO/TR 12300:2016 document for the mapping of nursing terminology. The referred document and related articles were used as an empirical framework. The study analyzed the content of the document, highlighting cardinality and equivalence principles. The standard presents conceptual and operational basis for mapping, with cardinality and equivalence as the support for the categorization of cross-terminology mapping in the area of nursing. Cardinality verifies candidate target terms to represent the source term, while the equivalence degree scale checks semantic correspondence. Among the principles included in the ISO/TR 12300:2016, cardinality and equivalence contribute to the accurate representation of the results of the cross-terminology mapping process and its use should decrease inconsistencies.

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Section: Iso/tr 12300:2016 Technical Documentmentioning

confidence: 99%

mentioning

confidence: 99%

ISO/TR 12300:2016 for clinical cross-terminology mapping: contribution to nursing

Torres

Gomes

Ronnau

et al. 2020

Rev. esc. enferm. USP

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“…The purpose of the application of a standardized terminology is to direct the work method, which influences the documentation of practices, guidance and support for clinical reasoning and the naming of the phenomena of the profession, thus contributing to the framework of specific knowledge 7 . In this context, The language of diagnoses statements, results and nursing interventions can be guided by terminologies, and the most used in the Brazilian scenario are the International Classification for Nursing Practice (CIPE®), the NANDA-International (NANDA-I), the Nursing Interventions Classification (NIC) and Nursing Outcomes Classification (NOC) [8][9] .…”

Section: Introductionmentioning

confidence: 99%

Nursing Diagnoses for People Living With Hiv: Relationships Between Terminologies

Rocha

Lima

Menezes

et al. 2022

Texto contexto - enferm.

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Objectives: to identify the relationships between nursing diagnoses for people living with HIV from the NANDA-International terminologies and International Classification for Nursing Practice (ICNP®) and to validate the equivalent meanings for diagnoses between language systems. Method: cross-sectional study, conducted in a reference hospital in northeastern Brazil, consisting of the following stages: 1) Identification of clinical findings through interviews and physical examinations with people living with HIV based on a validated script; 2) Structuring nursing diagnoses by means of Risner's clinical judgment; 3) Cross-mapping of nursing diagnosis statements with NANDA-I and ICNP® ; 4) Two rounds of Content validation using the Delphi technique with specialist nurses, for diagnoses with equivalent meanings between the systems. Data collection took place from August to November 2018. Results: in the preparation and identification of diagnoses, 135 nursing diagnoses were obtained, of which 62% (n=84) are included in the terminology of the ICNP® and 38% (n=51), from NANDA-International. For 81% (n=68) of the nursing diagnoses from the ICNP®, the absence of direct mapping in NANDA-international was identified, with 19% (n=16). The study showed that 47 diagnoses presented equivalent meanings. Conclusion: both systems enable the identification of nursing diagnoses accurately and have the ability to assist in the development of an individualized care plan for people living with HIV.

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“…With regard to automating mappings between ICNP terms and other terminologies, precoordinated terms should be considered in the UMLS in English [ 24 ]. Further, because the primitive terms of the ICNP are arranged in a 7-axis model, natural language processing (NLP) algorithms and techniques can support its mapping [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

“…In this context, the hypothesis of the study that is reported in this paper is as follows: mapping automation, through computational algorithms, collaborates with the manual mapping process. The goal of this study is to compare results of manual and automated term mapping processes to verify if the automated method is adequate to support the task of mapping, considering the challenges of the manual cross-term mapping process [ 25 ], the possible contribution of automated mapping to nursing terminologies [ 11 , 13 , 14 ], and the incipience of studies that compare manual and automated mappings.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Results of Manual and Automated Processes of Cross-Mapping Between Nursing Terms: Quantitative Study

et al. 2020

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Background 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. Objective The aim of this study was to compare the results of manual and automated term mapping processes. Methods 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. Results 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. Conclusions 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.

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Use of Computational Tools as Support to the Cross-Mapping Method Between Clinical Terminologies

Cited by 5 publications

References 19 publications

ISO/TR 12300:2016 for clinical cross-terminology mapping: contribution to nursing

ISO/TR 12300:2016 for clinical cross-terminology mapping: contribution to nursing

Nursing Diagnoses for People Living With Hiv: Relationships Between Terminologies

Comparison of the Results of Manual and Automated Processes of Cross-Mapping Between Nursing Terms: Quantitative Study

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