Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Medical Internet Research Année : 2020

Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic

Antoine Neuraz
Ivan Lerner
William Digan
  • Fonction : Auteur
Nicolas Paris
  • Fonction : Auteur
Rosy Tsopra
Alice Rogier
  • Fonction : Auteur
David Baudoin
  • Fonction : Auteur
Kevin Bretonnel Cohen
  • Fonction : Auteur
Anita Burgun
Bastien Rance

Résumé

A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity.

Dates et versions

hal-03119925 , version 1 (25-01-2021)

Identifiants

Citer

Antoine Neuraz, Ivan Lerner, William Digan, Nicolas Paris, Rosy Tsopra, et al.. Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic. Journal of Medical Internet Research, 2020, 22 (8), pp.e20773. ⟨10.2196/20773⟩. ⟨hal-03119925⟩
77 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More