This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2394) Machine Learning (1400) Outbreak and Pandemic Preparedness and Management (1679) Artificial Intelligence (1214) Natural Language Processing (650)

Published on in Vol 24, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34067, first published .
Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature With Unsupervised Word Embeddings and Machine Learning: Evidence-Based Study

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

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

Authors of this article:

Ridam Pal1 Author Orcid Image ;   Harshita Chopra2 Author Orcid Image ;   Raghav Awasthi1 Author Orcid Image ;   Harsh Bandhey1 Author Orcid Image ;   Aditya Nagori1, 3 Author Orcid Image ;   Tavpritesh Sethi1 Author Orcid Image
Article Authors Cited by (2) Tweetations (7) Metrics

Ridam Pal   1 , BTech ;   Harshita Chopra   2 , BTech ;   Raghav Awasthi   1 , BSc, MSc ;   Harsh Bandhey   1 , BTech ;   Aditya Nagori   1, 3 , PhD ;   Tavpritesh Sethi   1 , MBBS, PhD

1 Department of Computational Biology, Indraprastha Institute of Information Technology Delhi, New Delhi, India

2 Maharaja Surajmal Institute of Technology, Guru Gobind Singh Indraprastha University, New Delhi, India

3 Council of Scientific & Industrial Research-Institute of Genomics and Integrative Biology, New Delhi, India

Corresponding Author:

  • Tavpritesh Sethi, MBBS, PhD
  • Department of Computational Biology
  • Indraprastha Institute of Information Technology Delhi
  • Third Floor, New Academic Block
  • Okhla Industrial Estate, Phase-III
  • New Delhi, 110020
  • India
  • Phone: 91 9779908630
  • Email: tavpriteshsethi@iiitd.ac.in