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Mesbah, S. (author), Yang, J. (author), Sips, R.H.J. (author), Valle Torre, M. (author), Lofi, C. (author), Bozzon, A. (author), Houben, G.J.P.M. (author)
Social media provides a timely yet challenging data source for adverse drug reaction (ADR) detection. Existing dictionary-based, semi-supervised learning approaches are intrinsically limited by the coverage and maintainability of laymen health vocabularies. In this paper, we introduce a data augmentation approach that leverages variational...
conference paper 2019
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Manousogiannis, E. (author), Mesbah, S. (author), Baez Santamaria, Selene (author), Bozzon, A. (author), Sips, Robert-Jan (author)
This paper describes the system that team MYTOMORROWS-TU DELFT developed for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task 3, for the end-to-end normalization of ADR tweet mentions to their corresponding MEDDRA codes. For the first two steps, we reuse a state-of-theart approach, focusing our contribution on the final...
conference paper 2019