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


Shaitarova, Anastassia; Zaghir, Jamil; Lavelli, Alberto; Krauthammer, Michael; Rinaldi, Fabio (2023). Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey. Yearbook of medical informatics, 32(01):230-243.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:410 Linguistics
000 Computer science, knowledge & systems
Scopus Subject Areas:Health Sciences > Health Informatics
Health Sciences > Health Information Management
Uncontrolled Keywords:General Medicine, NMT
Language:English
Date:1 August 2023
Deposited On:12 Feb 2024 15:35
Last Modified:13 Feb 2024 21:00
Publisher:Schattauer
ISSN:0943-4747
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1055/s-0043-1768726
PubMed ID:38147865
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)