论文信息 - Visualization of Diseases at Risk in the COVID-19 Literature

Visualization of Diseases at Risk in the COVID-19 Literature

This paper presents a project, named VIDAR-19, able to extract automatically diseases from the CORD-19 dataset, and also diseases which might be considered as risk factors. The project relies on the ICD-11 classification of diseases maintained by the WHO. This nomenclature is used as a data source of the extraction mechanism, and also as the repository for the results. Developed for the COVID-19, the project has the ability to extract diseases at risk and to calculate relevant indicators. The outcome of the project is presented in a dashboard which enables the user to explore graphically diseases at risk which are put back in the classification hierarchy. Beyond the COVID-19, VIDAR has much broader applications and might be directly used for any corpus dealing with other pathologies.

Francis Wolinski | Francis Wolinski

[1] John D. Hunter,et al. Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[2] Stephen G. Kobourov,et al. Visualizing Large Graphs with Compound-Fisheye Views and Treemaps , 2004, GD.

[3] Vikash Singh,et al. Replace or Retrieve Keywords In Documents at Scale , 2017, ArXiv.

[4] Wes McKinney,et al. Data Structures for Statistical Computing in Python , 2010, SciPy.

[5] Oren Etzioni,et al. CORD-19: The Covid-19 Open Research Dataset , 2020, NLPCOVID19.

[6] Robert J. Cerfolio,et al. Factors associated with hospitalization and critical illness among 4,103 patients with COVID-19 disease in New York City , 2020, medRxiv.

[7] Jennifer Couzin-Frankel Meredith Wadman,et al. How does coronavirus kill? Clinicians trace a ferocious rampage through the body, from brain to toes , 2020 .