ABSTRACT
Developing countries have been plagued by recurrent cases of infectious disease outbreaks; coupled with the limitation of traditional disease control strategies, other approaches have been explored for disease control, with social media at the forefront. Data from this source is short, noisy, and informal in representation, thus, conventional natural language processing (NLP) methods are not well adapted for their structure. Hence, deep learning approaches for character-level word vector learning were explored to classify disease-related tweets, and an adaptive prediction model for outbreak monitoring was developed, using the Ebola virus disease as a case study. Our system showed better performance for the described task when compared with existing state-of-the-art architectures; also, our predictive model showed correlation with official reported cases, with early warning of fourteen days prior to official.
Obinwanne, E (2021). Applying Deep Learning Methods For Short Text Analysis In Disease Control. Afribary. Retrieved from https://tracking.afribary.com/works/applying-deep-learning-methods-for-short-text-analysis-in-disease-control
Obinwanne, Ezema "Applying Deep Learning Methods For Short Text Analysis In Disease Control" Afribary. Afribary, 13 Apr. 2021, https://tracking.afribary.com/works/applying-deep-learning-methods-for-short-text-analysis-in-disease-control. Accessed 24 Nov. 2024.
Obinwanne, Ezema . "Applying Deep Learning Methods For Short Text Analysis In Disease Control". Afribary, Afribary, 13 Apr. 2021. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/applying-deep-learning-methods-for-short-text-analysis-in-disease-control >.
Obinwanne, Ezema . "Applying Deep Learning Methods For Short Text Analysis In Disease Control" Afribary (2021). Accessed November 24, 2024. https://tracking.afribary.com/works/applying-deep-learning-methods-for-short-text-analysis-in-disease-control