An exploratory study of a text classification framework for Internet-based surveillance of emerging epidemics

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Conclusions: The proposed text classification framework utilizing randomly sampled unlabeled articles can facilitate a cost-effective approach to training machine learning classifiers in a real-life Internet-based biosurveillance project. We plan to examine this framework further using larger data sets and using articles in non-English languages. (Source: International Journal of Medical Informatics).