CC BY-NC-ND 4.0 · Yearb Med Inform 2018; 27(01): 193-198
DOI: 10.1055/s-0038-1667080
Section 9: Natural Language Processing
Georg Thieme Verlag KG Stuttgart

Expanding the Diversity of Texts and Applications: Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook

Aurélie Névéol
1   LIMSI, CNRS, Université Paris-Saclay, Orsay, France
Pierre Zweigenbaum
1   LIMSI, CNRS, Université Paris-Saclay, Orsay, France
Section Editors for the IMIA Yearbook Section on Clinical Natural Language Processing › Author Affiliations
Further Information

Publication History

Publication Date:
29 August 2018 (online)


Objectives: To summarize recent research and present a selection of the best papers published in 2017 in the field of clinical Natural Language Processing (NLP).

Methods: A survey of the literature was performed by the two editors of the NLP section of the International Medical Informatics Association (IMIA) Yearbook. Bibliographic databases PubMed and Association of Computational Linguistics (ACL) Anthology were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A total of 709 papers were automatically ranked and then manually reviewed based on title and abstract. A shortlist of 15 candidate best papers was selected by the section editors and peer-reviewed by independent external reviewers to come to the three best clinical NLP papers for 2017.

Results: Clinical NLP best papers provide a contribution that ranges from methodological studies to the application of research results to practical clinical settings. They draw from text genres as diverse as clinical narratives across hospitals and languages or social media.

Conclusions: Clinical NLP continued to thrive in 2017, with an increasing number of contributions towards applications compared to fundamental methods. Methodological work explores deep learning and system adaptation across language variants. Research results continue to translate into freely available tools and corpora, mainly for the English language.

  • References

  • In the reference list below, papers that were shortlisted as best paper candidates are marked with a *.
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