@InProceedings{nguyen-EtAl:2018:LOUHI,
  author    = {Nguyen, Hoang  and  Sugiyama, Kazunari  and  Kan, Min-Yen  and  Halder, Kishaloy},
  title     = {Treatment Side Effect Prediction from Online User-generated Content},
  booktitle = {Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {12--21},
  abstract  = {With Health 2.0, patients and caregivers increasingly seek information regarding possible drug side effects during their medical treatments in online health communities. These online communities are helpful platforms for non-professional medical opinions, yet pose risk of being unreliable in quality and insufficient in quantity to cover the wide range of potential drug reactions. Current approaches to analysing such user-generated content in online forums heavily rely on feature engineering of both documents and users, and often overlook the relationships between posts within a common discussion thread. Inspired by recent advancements, we propose a neural architecture that models the textual content of user-generated documents and user experiences in online communities to predict side effects during treatment. Experimental results show that our proposed architecture outperforms baseline models.},
  url       = {http://www.aclweb.org/anthology/W18-5602}
}