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

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...

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Detalles Bibliográficos
Autores principales: Grabar, Natalia, Grouin, Cyril
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2019
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697498/
https://www.ncbi.nlm.nih.gov/pubmed/31419835
http://dx.doi.org/10.1055/s-0039-1677937
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author Grabar, Natalia
Grouin, Cyril
author_facet Grabar, Natalia
Grouin, Cyril
author_sort Grabar, Natalia
collection PubMed
description 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.
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spelling pubmed-66974982019-08-19 A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook Grabar, Natalia Grouin, Cyril Yearb Med Inform 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. Georg Thieme Verlag KG 2019-08 2019-08-16 /pmc/articles/PMC6697498/ /pubmed/31419835 http://dx.doi.org/10.1055/s-0039-1677937 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.
spellingShingle Grabar, Natalia
Grouin, Cyril
A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook
title A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook
title_full A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook
title_fullStr A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook
title_full_unstemmed A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook
title_short A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook
title_sort year of papers using biomedical texts: findings from the section on natural language processing of the imia yearbook
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697498/
https://www.ncbi.nlm.nih.gov/pubmed/31419835
http://dx.doi.org/10.1055/s-0039-1677937
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