Treatment Side Effect Prediction from Online User-generated Content

Van Hoang Nguyen, Kazunari Sugiyama, Min-Yen Kan, Kishaloy Halder


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.
Anthology ID:
W18-5602
Volume:
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | Louhi | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–21
Language:
URL:
https://aclanthology.org/W18-5602
DOI:
10.18653/v1/W18-5602
Bibkey:
Cite (ACL):
Van Hoang Nguyen, Kazunari Sugiyama, Min-Yen Kan, and Kishaloy Halder. 2018. Treatment Side Effect Prediction from Online User-generated Content. In Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis, pages 12–21, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Treatment Side Effect Prediction from Online User-generated Content (Nguyen et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5602.pdf