CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 218-222
DOI: 10.1055/s-0039-1677937
Section 10: Natural Language Processing
Georg Thieme Verlag KG Stuttgart

A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook

Natalia Grabar
1  LIMSI, CNRS, Université Paris-Saclay, Orsay, France
2  STL, CNRS, Université de Lille, Villeneuve-d'Ascq, France
Cyril Grouin
1  LIMSI, CNRS, Université Paris-Saclay, Orsay, France
Section Editors for the IMIA Yearbook Section on Natural Language Processing› Institutsangaben
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16. August 2019 (online)



Objectives: To analyze the content of publications within the medical Natural Language Processing (NLP) domain in 2018.

Methods: Automatic and manual pre-selection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Results: Two best papers have been selected this year. One dedicated to the generation of multi- documents summaries and another dedicated to the generation of imaging reports. We also proposed an analysis of the content of main research trends of NLP publications in 2018.

Conclusions: The year 2018 is very rich with regard to NLP issues and topics addressed. It shows the will of researchers to go towards robust and reproducible results. Researchers also prove to be creative for original issues and approaches.