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        <title>Treatment Side Effect Prediction from Online User-generated Content</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Treatment Side Effect Prediction from Online User-generated Content
%A Nguyen, Van Hoang
%A Sugiyama, Kazunari
%A Kan, Min-Yen
%A Halder, Kishaloy
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Rinaldi, Fabio
%S Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F nguyen-etal-2018-treatment
%X 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.
%R 10.18653/v1/W18-5602
%U https://aclanthology.org/W18-5602
%U https://doi.org/10.18653/v1/W18-5602
%P 12-21

Markdown (Informal)

[Treatment Side Effect Prediction from Online User-generated Content](https://aclanthology.org/W18-5602) (Nguyen et al., Louhi 2018)

• Treatment Side Effect Prediction from Online User-generated Content (Nguyen et al., Louhi 2018)

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.