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• W2056876304 abstract "Adverse drug reactions (ADRs) are causing a substantial amount of hospital admissions and deaths, which cannot be underestimated. Drug-drug interactions (DDIs) are an important patient safety problem and have been reported to cause a large portion of patient adverse events resulting in warning notices or the withdrawal of many drugs from the market. Currently, DDIs detection mainly depends on four kinds of data sources - clinical trial data, spontaneous reporting systems, electronic medical records, and chemical/pharmacologic databases, all of which have some limitations such as cohort biases, low reporting ratio, access issue, etc. In this study, we propose to detect DDIs signals from consumer contributed contents in online healthcare communities using associations mining. We conduct an experiment with thirteen drugs and three DDI associations. Leverage, lift and interaction ratio are used in the experiment. Drug Bank is used as gold standard to test the performance of the approach. The results show that our techniques are promising to detect signals of DDIs and the proposed measure, interaction ratio, performs better than leverage and lift." @default.
• W2056876304 created "2016-06-24" @default.
• W2056876304 creator A5046308214 @default.
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• W2056876304 date "2013-09-01" @default.
• W2056876304 modified "2023-10-06" @default.
• W2056876304 title "Harnessing Social Media for Drug-Drug Interactions Detection" @default.
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• W2056876304 doi "https://doi.org/10.1109/ichi.2013.10" @default.
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