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CC BY-NC-ND 4.0 · Yearb Med Inform 2023; 32(01): 230-243
DOI: 10.1055/s-0043-1768726
Section 10: Natural Language Processing
Survey

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Anastassia Shaitarova‡   *
1   Department of Computational Linguistics, University of Zurich, Zurich, Switzerland
,
Jamil Zaghir‡   *
2   Division of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland
3   Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
,
Alberto Lavelli
4   Natural Language Processing Research Unit, Center for Digital Health and Wellbeing, Fondazione Bruno Kessler, Trento, Italy
,
Michael Krauthammer
5   Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
6   Biomedical Informatics, University Hospital Zurich, Zurich, Switzerland
,
Fabio Rinaldi
4   Natural Language Processing Research Unit, Center for Digital Health and Wellbeing, Fondazione Bruno Kessler, Trento, Italy
5   Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
7   Dalle Molle Institute for Artificial Intelligence Research, Lugano, Switzerland
8   Swiss Institute of Bioinformatics
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Summary

Objectives: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks.

Methods: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae.

Results: Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.

Keywords

Multilingualism - natural language processing - datasets as topic - language models - shared tasks

* These authors contributed equally to this work


6 We use the standardized nomenclature ISO 639-3 for the language codes (https://iso639-3.sil.org/code_tables/639/data).




Publication History

Article published online:
26 December 2023

© 2023. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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