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- W2537033652 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." @default.
- W2537033652 created "2016-10-28" @default.
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- W2537033652 date "2016-08-01" @default.
- W2537033652 modified "2023-10-17" @default.
- W2537033652 title "Using constrained information entropy to detect rare adverse drug reactions from medical forums" @default.
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- W2537033652 doi "https://doi.org/10.1109/embc.2016.7591228" @default.
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