Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China

Abstract

Background With the development and application of medical information system, semantic interoperability is essential for accurate and advanced health-related computing and electronic health record (EHR) information sharing. The openEHR approach can improve semantic interoperability. One key improvement of openEHR is that it allows for the use of existing archetypes. The crucial problem is how to improve the precision and resolve ambiguity in the archetype retrieval. Method Based on the query expansion technology and Word2Vec model in Nature Language Processing (NLP), we propose to find synonyms as substitutes for original search terms in archetype retrieval. Test sets in different medical professional level are used to verify the feasibility. Result Applying the approach to each original search term (n = 120) in test sets, a total of 69,348 substitutes were constructed. Precision at 5 (P@5) was improved by 0.767, on average. For the best result, the P@5 was up to 0.975. Conclusions We introduce a novel approach that using NLP technology and corpus to find synonyms as substitutes for original search terms. Compared to simply mapping the element contained in openEHR to an external dictionary, this approach could greatly improve precision and resolve ambiguity in retrieval tasks. This is helpful to promote the application of openEHR and advance EHR information sharing.

Análise de conteúdo à parametrização portuguesa relacionada com o Processo Respiratório

“…Although the use of standardized language is a significant upgrade to nursing practice, the challenge of nursing information systems is to reduce redundancy and to improve semantic interoperability (Sun et al, 2021). We are now facing a double challenge: improving semantic interoperability within and between the system and aggregate information about nursing care, reducing diagnostic and intervention variability, improving information intelligibility (Silva et al, 2014).…”

“…Thus, reducing redundancy by using a standardized language that promotes the capacity to communicate between different information systems and contributes to supporting nursing care, and effective nursing decision-making enabling easy retrieval and analysis of nursing data (Lingtong et al, 2018). Integrating nursing data in large datasets represents a considerable challenge that can be achieved using a controlled taxonomy instead of unstructured text data, helping aggregate terms (Sun et al, 2021).…”