Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter

Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter

Abstract Background Lyme disease is one of the most commonly reported infectious diseases in the United States (US), accounting for more than $$90\%$$ 90 % of all vector-borne diseases in North America. Objective In this paper, self-reported tweets on Twitter were analyzed in order to predict potent...

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Bibliographic Details
Main Authors: Srikanth Boligarla, Elda Kokoè Elolo Laison, Jiaxin Li, Raja Mahadevan, Austen Ng, Yangming Lin, Mamadou Yamar Thioub, Bruce Huang, Mohamed Hamza Ibrahim, Bouchra Nasri
Format: Article
Language:English
Published: BMC 2023-10-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Lyme disease
Twitter
BERT
Incidence Rate
Online Access:https://doi.org/10.1186/s12911-023-02315-z

Internet

https://doi.org/10.1186/s12911-023-02315-z

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