A Year of Papers Using Biomedical Texts

Cyril Grouin (1) , Natalia Grabar (2)

1 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
2 STL - Savoirs, Textes, Langage (STL) - UMR 8163

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.

Domaines: Informatique [cs]; Sciences du Vivant [q-bio]

Cyril Grouin, Natalia Grabar. A Year of Papers Using Biomedical Texts: Findings from the Section on Clinical Natural Language Processing of the nternational Medical Informatics Association Yearbook. IMIA Yearbook of Medical Informatics, 2020, 29 (1), pp.221-225. ⟨10.1055/s-0040-1701997⟩. ⟨hal-03095125⟩