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

Infodemiology and Infoveillance (1436) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1081) Machine Learning (1671) Natural Language Processing (781) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1761) Artificial Intelligence (1653)

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
Article Authors Cited by (1) Tweetations (2) Metrics
All Time | Last Month | Last Six Months | Last Year
Tom Bellis Tom Bellis @tommybellis

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis https://t.co/CmhWD2sCkU https://t.co/4P1oRnfKS7

4:12 PM · Oct 16, 2023

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JMIR Publications JMIR Publications @jmirpub

New in JMIR: Identifying Potential Lyme #Disease Cases Using Self-Reported Worldwide #Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis https://t.co/vkYES12YeU https://t.co/I6Kqrr2DcD

6:15 PM · Oct 16, 2023

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