Van Hoang Nguyen
Treatment Side Effect Prediction from Online User-generated Content
Van Hoang Nguyen | Kazunari Sugiyama | Min-Yen Kan | Kishaloy Halder
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
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.