Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature With Unsupervised Word Embeddings and Machine Learning: Evidence-Based Study

BACKGROUND: Evidence from peer-reviewed literature is the cornerstone for designing responses to global threats such as COVID-19. In massive and rapidly growing corpuses, such as COVID-19 publications, assimilating and synthesizing information is challenging. Leveraging a robust computational pipeli...

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Detalles Bibliográficos
Autores principales: Pal, Ridam, Chopra, Harshita, Awasthi, Raghav, Bandhey, Harsh, Nagori, Aditya, Sethi, Tavpritesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629347/
https://www.ncbi.nlm.nih.gov/pubmed/36040993
http://dx.doi.org/10.2196/34067

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629347/
https://www.ncbi.nlm.nih.gov/pubmed/36040993
http://dx.doi.org/10.2196/34067

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