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

BMC Medical Informatics and Decision Making

Jing Li¹  Xiaolin Diao²  Fei Zhang²  Wei Zhao²  Bo Sun²  Yicheng Yang³  Ting Shu⁴ 

[1] Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China;Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, 100037, Beijing, China;Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, 100191, Beijing, China;National Institute of Hospital Administration, National Health Commission, Building 3, yard 6, Shouti South Road, Haidian, 100044, Beijing, China;

关键词: OpenEHR;    Nature language processing;    Information retrieval;    Interoperability;   

DOI  :  10.1186/s12911-021-01554-2

来源: Springer

【 摘 要 】

BackgroundWith 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.MethodBased 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.ResultApplying 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.ConclusionsWe 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.

【 授权许可】

CC BY