About: A Year of Papers Using Biomedical Texts:: Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A Year of Papers Using Biomedical Texts:: Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook
Creator	Grabar, Natalia Grouin, Cyril
Source	Medline; PMC
abstract	Objectives : Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field. Methods : We performed an automatic and manual pre-selection of papers to be reviewed and finally selected the best NLP papers of the year. We also propose an analysis of the content of NLP publications in 2019. Results : Three best papers have been selected this year including the generation of synthetic record texts in Chinese, a method to identify contradictions in the literature, and the BioBERT word representation. Conclusions : The year 2019 was very rich and various NLP issues and topics were addressed by research teams. This shows the will and capacity of researchers to move towards robust and reproducible results. Researchers also prove to be creative in addressing original issues with relevant approaches.
has issue date	2020-08-21(xsd:dateTime)
bibo:doi	10.1055/s-0040-1701997
bibo:pmid	32823319
has license	cc-by-nc-nd
sha1sum (hex)	90147c26a2ca89ddc175f7bbd591e4991c3b2b54
schema:url	https://doi.org/10.1055/s-0040-1701997
resource representing a document's title	A Year of Papers Using Biomedical Texts:: Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook
has PubMed Central identifier	PMC7442503
has PubMed identifier	32823319
schema:publication	Yearb Med Inform
resource representing a document's body	covid:90147c26a2ca89ddc175f7bbd591e4991c3b2b54#body_text
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