title: Using constrained information entropy to detect rare adverse drug reactions from medical forums.
abstract: Adverse drug reactions (ADRs) detection is critical to avoid malpractices yet challenging due to its uncertainty in pre-marketing review and the underreporting in post-marketing surveillance. To conquer this predicament, social media based ADRs detection methods have been proposed recently. However, existing researches are mostly co-occurrence based methods and face several issues, in particularly, leaving out the rare ADRs and unable to distinguish irrelevant ADRs. In this work, we introduce a constrained information entropy (CIE) method to solve these problems. CIE first recognizes the drug-related adverse reactions using a predefined keyword dictionary and then captures high- and low-frequency (rare) ADRs by information entropy. Extensive experiments on medical forums dataset demonstrate that CIE outperforms the state-of-the-art co-occurrence based methods, especially in rare ADRs detection.
authors.lastname: Yi Zheng
authors.lastname: Chaowang Lan
authors.lastname: Hui Peng
authors.lastname: Jinyan Li
mesh_terms: D000465:Algorithms; D019992:Databases as Topic; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis*; D019277:Entropy; D006801:Humans; D061108:Social Media