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In recent years, Twitter has been applied to monitor diseases through its facility to monitor users' comments and concerns in real-time. The analysis of tweets for disease mentions should reflect not only user specific concerns but also disease outbreaks. This requires the use of standard terminological resources and can be focused on selected geographic locations. In our study, we differentiate between hospital and airport locations to better distinguish disease outbreaks from background mentions of disease concerns.
Pubmed ID: 29895320
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Program to map biomedical text to the UMLS Metathesaurus and to discover Metathesaurus concepts referred to in text based on symbolic, natural-language processing and computational-linguistic techniques.
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