Clinical Natural Language Processing in languages other than English: opportunities and challenges

By Aurélie Névéol, Hercules Dalianis, Guergana K. Savova, Pierre Zweigenbaum. Journal of Biomedical Semantics, 2018

306 citations recorded by the source corpus.

Natural language processing applied to clinical text or aimed at a clinical outcome has been thriving in recent years. This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English. Recent studies are summarized to offer insights and outline opportunities in this area. We envision three groups of intended readers: (1) NLP researchers leveraging experience gained in other languages, (2) NLP researchers faced with establishing clinical text processing in a language other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation. We review work in clinical NLP in languages other than English. We classify these studies into three groups: (i) studies describing the development of new NLP systems or components de novo, (ii) studies describing the adaptation of NLP architectures developed for English to another language, and (iii) studies focusing on a particular clinical application. We show the advantages and drawbacks of each method, and highlight the appropriate application context. Finally, we identify major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/47dba53fe8ec69f90b4abfa263be70eef3fdee7f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

Citations in the corpus

Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “The challenges and opportunities of clinical NLP for languages other than English along with a review of clinical NLP techniques is presented in [79].”
Large language models are few-shot clinical information extractors (2022, methodology): “Further, as is unfortunately common in clinical NLP, we only tested in English, leaving testing the ability of LLMs to operate in different languages to future work (Névéol et al., 2018).”