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

JMIR Theme Issue: COVID-19 Special Issue (2088) Machine Learning (1004) Outbreak and Pandemic Preparedness and Management (1454) Artificial Intelligence (665) Natural Language Processing (490)

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 Pal 1 Author Orcid Image ;   Harshita Chopra 2 Author Orcid Image ;   Raghav Awasthi 1 Author Orcid Image ;   Harsh Bandhey 1 Author Orcid Image ;   Aditya Nagori 1, 3 Author Orcid Image ;   Tavpritesh Sethi 1 Author Orcid Image
Article Authors Cited by 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 , IN

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

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

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
  • IN
  • Phone: 91 9779908630
  • Email: tavpriteshsethi@iiitd.ac.in