Applying Deep Learning Methods For Short Text Analysis In Disease Control
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Applying Deep Learning Methods For Short Text Analysis In Disease Control
ABSTRACT
Developing countries have been plagued by recurring infectious disease epidemics; along with the limitations of traditional disease management tactics, various approaches to disease control have been investigated, with social media at the forefront.
Because the data from this source is brief, noisy, and informal in representation, traditional natural language processing (NLP) approaches are inadequate for its structure.
As a result, deep learning algorithms for character-level word vector learning were investigated to categorise disease-related tweets, and an adaptive prediction model for epidemic monitoring was created, with the Ebola virus disease as a case study.
Our system outperformed existing state-of-the-art designs for the given job; additionally, our predictive model demonstrated correlation with officially reported instances, with an early warning of fourteen days.
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