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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2021

Open Access 01-12-2021 | Research article

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

Authors: Bo Sun, Fei Zhang, Jing Li, Yicheng Yang, Xiaolin Diao, Wei Zhao, Ting Shu

Published in: BMC Medical Informatics and Decision Making | Issue 1/2021

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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.
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Metadata
Title
Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China
Authors
Bo Sun
Fei Zhang
Jing Li
Yicheng Yang
Xiaolin Diao
Wei Zhao
Ting Shu
Publication date
01-12-2021
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2021
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-021-01554-2

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