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

Infodemiology and Infoveillance (1269) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1010) Machine Learning (1370) Natural Language Processing (636) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1552) Artificial Intelligence (1186)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47014, first published .
Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

Authors of this article:

Elda Kokoe Elolo Laison1 Author Orcid Image ;   Mohamed Hamza Ibrahim2 Author Orcid Image ;   Srikanth Boligarla3 Author Orcid Image ;   Jiaxin Li3 Author Orcid Image ;   Raja Mahadevan3 Author Orcid Image ;   Austen Ng3 Author Orcid Image ;   Venkataraman Muthuramalingam3 Author Orcid Image ;   Wee Yi Lee3 Author Orcid Image ;   Yijun Yin3 Author Orcid Image ;   Bouchra R Nasri1 Author Orcid Image
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