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      Neural machine translation of clinical texts between long distance languages

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          Abstract

          Objective

          To analyze techniques for machine translation of electronic health records (EHRs) between long distance languages, using Basque and Spanish as a reference. We studied distinct configurations of neural machine translation systems and used different methods to overcome the lack of a bilingual corpus of clinical texts or health records in Basque and Spanish.

          Materials and Methods

          We trained recurrent neural networks on an out-of-domain corpus with different hyperparameter values. Subsequently, we used the optimal configuration to evaluate machine translation of EHR templates between Basque and Spanish, using manual translations of the Basque templates into Spanish as a standard. We successively added to the training corpus clinical resources, including a Spanish-Basque dictionary derived from resources built for the machine translation of the Spanish edition of SNOMED CT into Basque, artificial sentences in Spanish and Basque derived from frequently occurring relationships in SNOMED CT, and Spanish monolingual EHRs. Apart from calculating bilingual evaluation understudy (BLEU) values, we tested the performance in the clinical domain by human evaluation.

          Results

          We achieved slight improvements from our reference system by tuning some hyperparameters using an out-of-domain bilingual corpus, obtaining 10.67 BLEU points for Basque-to-Spanish clinical domain translation. The inclusion of clinical terminology in Spanish and Basque and the application of the back-translation technique on monolingual EHRs significantly improved the performance, obtaining 21.59 BLEU points. This was confirmed by the human evaluation performed by 2 clinicians, ranking our machine translations close to the human translations.

          Discussion

          We showed that, even after optimizing the hyperparameters out-of-domain, the inclusion of available resources from the clinical domain and applied methods were beneficial for the described objective, managing to obtain adequate translations of EHR templates.

          Conclusion

          We have developed a system which is able to properly translate health record templates from Basque to Spanish without making use of any bilingual corpus of clinical texts or health records.

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          Author and article information

          Journal
          J Am Med Inform Assoc
          J Am Med Inform Assoc
          jamia
          Journal of the American Medical Informatics Association : JAMIA
          Oxford University Press
          1067-5027
          1527-974X
          December 2019
          23 July 2019
          23 July 2020
          : 26
          : 12
          : 1478-1487
          Affiliations
          Faculty of Informatics, Computer Languages and Systems, Ixa Research Group, University of the Basque Country (UPV/EHU), Donostia, Spain
          Author notes
          Corresponding Author: Xabier Soto, MS, Faculty of Informatics, Computer Languages and Systems, Ixa Research Group, University of the Basque Country (UPV/EHU), Manuel Lardizabal 1, 20018 Donostia, Spain ( xabier.soto@ 123456ehu.eus )
          Article
          PMC7647170 PMC7647170 7647170 ocz110
          10.1093/jamia/ocz110
          7647170
          31334764
          © The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com

          This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

          Page count
          Pages: 10
          Funding
          Funded by: Spanish Ministry of Economy and Competitiveness
          Categories
          Research and Applications

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