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Title: Neural side effect discovery from user credibility and experience-assessed online health discussions
Authors: Van-Hoang Nguyen
Kazunari Sugiyama 
Min-Yen Kan 
Kishaloy Halder 
Keywords: Online health communities
Drug side effect discovery
Credibility analysis
Deep learning
Natural language processing
Issue Date: 8-Jul-2020
Publisher: Journal of Biomedical Semantics
Citation: Van-Hoang Nguyen, Kazunari Sugiyama, Min-Yen Kan, Kishaloy Halder (2020-07-08). Neural side effect discovery from user credibility and experience-assessed online health discussions. Journal of Biomedical Semantics 11 (5). ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Health 2.0 allows patients and caregivers to conveniently seek medical information and advice via e-portals and online discussion forums, especially regarding potential drug side effects. Although online health communities are helpful platforms for obtaining non-professional opinions, they pose risks in communicating unreliable and insufficient information in terms of quality and quantity. Existing methods in extracting user-reported adverse drug reactions (ADRs) in online health forums are not only insufficiently accurate as they disregard user credibility and drug experience, but are also expensive as they rely on supervised ground truth annotation of individual statement. We propose a NEural ArchiTecture for Drug side effect prediction (NEAT), which is optimized on the task of drug side effect discovery based on a complete discussion while being attentive to user credibility and experience, thus, addressing the mentioned shortcomings. We train our neural model in a self-supervised fashion using ground truth drug side effects from NEAT learns to assign each user a score that is descriptive of their credibility and highlights the critical textual segments of their post.
Source Title: Journal of Biomedical Semantics
DOI: 10.1186/s13326-020-00221-1
Rights: Attribution 4.0 International
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