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.
Highlights
Seeking medical opinions from online health communities has become commonplace: 71% of age 18–29 reported consulting online health opinion (Fox and Duggan, 2013)
The stricter Experiment 2 shows similar performance trends. These statistics indicate that our model successfully learns to include more side effects in its prediction, where many are relevant to the ground truth
This is consistent with our hypothesis that considering author experience of each post is effective in predicting out-of-context side effects
Summary
Seeking medical opinions from online health communities has become commonplace: 71% of age 18–29 (equivalent to 59% of all U.S adults) reported consulting online health opinion (Fox and Duggan, 2013) These opinions come from an estimated twenty to one hundred thousand healthrelated websites (Diaz et al, 2002), inclusive of online health communities that network patients with each other to provide information and social support (Johnston et al, 2013). Platforms such as HealthBoards and MedHelp feature users reporting their own health experiences, inclusive of their self-reviewed drugs and medical treatments.
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