@inproceedings{nguyen-etal-2018-treatment,
    title = "Treatment Side Effect Prediction from Online User-generated Content",
    author = "Nguyen, Van Hoang  and
      Sugiyama, Kazunari  and
      Kan, Min-Yen  and
      Halder, Kishaloy",
    editor = "Lavelli, Alberto  and
      Minard, Anne-Lyse  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-5602",
    doi = "10.18653/v1/W18-5602",
    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 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. Existing approaches which analyze 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.",
}