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Applying Deep Learning Methods for Short Text Analysis in Disease Control

dc.contributor.authorEzema, Abraham Obinwanne
dc.description.abstractDeveloping 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.en_US
dc.description.sponsorshipAUST and AfDB.en_US
dc.subject2017 Computer Science and Engineering Thesesen_US
dc.subjectEzema Abraham Obinwanneen_US
dc.subjectProf. Ekpe Okoraforen_US
dc.subjectDeep learningen_US
dc.subjectdisease controlen_US
dc.subjectshort text analysisen_US
dc.subjectword vector learningen_US
dc.titleApplying Deep Learning Methods for Short Text Analysis in Disease Controlen_US

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  • Computer Science105

    This collection contains Computer Science Student's Theses from 2009-2022

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